Recover Corrupted Data Through Speculative Bitflip And Cross-Validation

ABSTRACT

Recovering corrupted data through speculative bitflip and cross-validation, including altering a first data portion stored in a storage device, wherein the first data portion contains an error; calculating a hash value using the altered first data portion; comparing the hash value calculated using the altered first data portion to a stored hash value for the first data portion; and based on the comparing, determining whether the altering corrects the error in the first data portion.

CROSS REFERENCE TO RELATED APPLICATION

This is a continuation in-part application for patent entitled to afiling date and claiming the benefit of earlier-filed U.S. patentapplication Ser. No. 17/587,514, filed Jan. 28, 2022, hereinincorporated by reference in its entirety.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 3E illustrates an example of a fleet of storage systems forproviding storage services (also referred to herein as ‘data services’)in accordance with some embodiments.

FIG. 3F illustrates an example container system.

FIG. 4 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 5 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 6 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 7 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 8 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 9 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 10 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 11 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 12 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 13 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 14 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 15 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 16 sets forth a flow chart illustrating an example method ofrestoring lost data in accordance with some embodiments of the presentdisclosure.

FIG. 17 sets forth a flow chart illustrating an example method ofrecovering corrupted data through speculative bitflip andcross-validation in accordance with some embodiments of the presentdisclosure.

FIG. 18 sets forth a flow chart illustrating an example method ofrecovering corrupted data through speculative bitflip andcross-validation in accordance with some embodiments of the presentdisclosure.

FIG. 19 sets forth a flow chart illustrating an example method ofrecovering corrupted data through speculative bitflip andcross-validation in accordance with some embodiments of the presentdisclosure.

FIG. 20 sets forth a flow chart illustrating an example method ofrecovering corrupted data through speculative bitflip andcross-validation in accordance with some embodiments of the presentdisclosure.

FIG. 21 sets forth a flow chart illustrating an example method ofrecovering corrupted data through speculative bitflip andcross-validation in accordance with some embodiments of the presentdisclosure.

FIG. 22 sets forth a flow chart illustrating an example method ofrecovering corrupted data through speculative bitflip andcross-validation in accordance with some embodiments of the presentdisclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for restoring lost data inaccordance with embodiments of the present disclosure are described withreference to the accompanying drawings, beginning with FIG. 1A. FIG. 1Aillustrates an example system for data storage, in accordance with someimplementations. System 100 (also referred to as “storage system”herein) includes numerous elements for purposes of illustration ratherthan limitation. It may be noted that system 100 may include the same,more, or fewer elements configured in the same or different manner inother implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in some implementations. Storage array 102A and102B may include one or more storage array controllers 110A-D (alsoreferred to as “controller” herein). A storage array controller 110A-Dmay be embodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of storage deviceutilization and performance, performing redundancy operations, such asRedundant Array of Independent Drives (‘RAID’) or RAID-like dataredundancy operations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In some implementations, storage arraycontroller 110A-D may include an I/O controller or the like that couplesthe storage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B may include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. In someimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In some implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘PIE’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drives 171A-F.

In some implementations, storage array controllers 110A-D may offloaddevice management responsibilities from storage drives 171A-F of storagearray 102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drives 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In some implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instant, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In some implementations, storage array controllers 110A-D arecommunicatively coupled, via a midplane (not shown), to one or morestorage drives 171A-F and to one or more NVRAM devices (not shown) thatare included as part of a storage array 102A-B. The storage arraycontrollers 110A-D may be coupled to the midplane via one or more datacommunication links and the midplane may be coupled to the storagedrives 171A-F and the NVRAM devices via one or more data communicationslinks. The data communications links described herein are collectivelyillustrated by data communications links 108A-D and may include aPeripheral Component Interconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may be similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In some implementations, storage array controller 101 includes one ormore host bus adapters 103A-C that are coupled to the processing device104 via a data communications link 105A-C. In some implementations, hostbus adapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In some implementations, storage array controller 101 may include a hostbus adapter 114 that is coupled to an expander 115. The expander 115 maybe used to attach a host system to a larger number of storage drives.The expander 115 may, for example, be a SAS expander utilized to enablethe host bus adapter 114 to attach to storage drives in animplementation where the host bus adapter 114 is embodied as a SAScontroller.

In some implementations, storage array controller 101 may include aswitch 116 coupled to the processing device 104 via a datacommunications link 109. The switch 116 may be a computer hardwaredevice that can create multiple endpoints out of a single endpoint,thereby enabling multiple devices to share a single endpoint. The switch116 may, for example, be a PCIe switch that is coupled to a PCIe bus(e.g., data communications link 109) and presents multiple PCIeconnection points to the midplane.

In some implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

In some implementations, storage drive 171A-F may be one or more zonedstorage devices. In some implementations, the one or more zoned storagedevices may be a shingled HDD. In some implementations, the one or morestorage devices may be a flash-based SSD. In a zoned storage device, azoned namespace on the zoned storage device can be addressed by groupsof blocks that are grouped and aligned by a natural size, forming anumber of addressable zones. In some implementations utilizing an SSD,the natural size may be based on the erase block size of the SSD. Insome implementations, the zones of the zoned storage device may bedefined during initialization of the zoned storage device. In someimplementations, the zones may be defined dynamically as data is writtento the zoned storage device.

In some implementations, zones may be heterogeneous, with some zoneseach being a page group and other zones being multiple page groups. Insome implementations, some zones may correspond to an erase block andother zones may correspond to multiple erase blocks. In animplementation, zones may be any combination of differing numbers ofpages in page groups and/or erase blocks, for heterogeneous mixes ofprogramming modes, manufacturers, product types and/or productgenerations of storage devices, as applied to heterogeneous assemblies,upgrades, distributed storages, etc. In some implementations, zones maybe defined as having usage characteristics, such as a property ofsupporting data with particular kinds of longevity (very short lived orvery long lived, for example). These properties could be used by a zonedstorage device to determine how the zone will be managed over the zone'sexpected lifetime.

It should be appreciated that a zone is a virtual construct. Anyparticular zone may not have a fixed location at a storage device. Untilallocated, a zone may not have any location at a storage device. A zonemay correspond to a number representing a chunk of virtually allocatablespace that is the size of an erase block or other block size in variousimplementations. When the system allocates or opens a zone, zones getallocated to flash or other solid-state storage memory and, as thesystem writes to the zone, pages are written to that mapped flash orother solid-state storage memory of the zoned storage device. When thesystem closes the zone, the associated erase block(s) or other sizedblock(s) are completed. At some point in the future, the system maydelete a zone which will free up the zone's allocated space. During itslifetime, a zone may be moved around to different locations of the zonedstorage device, e.g., as the zoned storage device does internalmaintenance.

In some implementations, the zones of the zoned storage device may be indifferent states. A zone may be in an empty state in which data has notbeen stored at the zone. An empty zone may be opened explicitly, orimplicitly by writing data to the zone. This is the initial state forzones on a fresh zoned storage device, but may also be the result of azone reset. In some implementations, an empty zone may have a designatedlocation within the flash memory of the zoned storage device. In animplementation, the location of the empty zone may be chosen when thezone is first opened or first written to (or later if writes arebuffered into memory). A zone may be in an open state either implicitlyor explicitly, where a zone that is in an open state may be written tostore data with write or append commands. In an implementation, a zonethat is in an open state may also be written to using a copy commandthat copies data from a different zone. In some implementations, a zonedstorage device may have a limit on the number of open zones at aparticular time.

A zone in a closed state is a zone that has been partially written to,but has entered a closed state after issuing an explicit closeoperation. A zone in a closed state may be left available for futurewrites, but may reduce some of the run-time overhead consumed by keepingthe zone in an open state. In some implementations, a zoned storagedevice may have a limit on the number of closed zones at a particulartime. A zone in a full state is a zone that is storing data and can nolonger be written to. A zone may be in a full state either after writeshave written data to the entirety of the zone or as a result of a zonefinish operation. Prior to a finish operation, a zone may or may nothave been completely written. After a finish operation, however, thezone may not be opened a written to further without first performing azone reset operation.

The mapping from a zone to an erase block (or to a shingled track in anHDD) may be arbitrary, dynamic, and hidden from view. The process ofopening a zone may be an operation that allows a new zone to bedynamically mapped to underlying storage of the zoned storage device,and then allows data to be written through appending writes into thezone until the zone reaches capacity. The zone can be finished at anypoint, after which further data may not be written into the zone. Whenthe data stored at the zone is no longer needed, the zone can be resetwhich effectively deletes the zone's content from the zoned storagedevice, making the physical storage held by that zone available for thesubsequent storage of data. Once a zone has been written and finished,the zoned storage device ensures that the data stored at the zone is notlost until the zone is reset. In the time between writing the data tothe zone and the resetting of the zone, the zone may be moved aroundbetween shingle tracks or erase blocks as part of maintenance operationswithin the zoned storage device, such as by copying data to keep thedata refreshed or to handle memory cell aging in an SSD.

In some implementations utilizing an HDD, the resetting of the zone mayallow the shingle tracks to be allocated to a new, opened zone that maybe opened at some point in the future. In some implementations utilizingan SSD, the resetting of the zone may cause the associated physicalerase block(s) of the zone to be erased and subsequently reused for thestorage of data. In some implementations, the zoned storage device mayhave a limit on the number of open zones at a point in time to reducethe amount of overhead dedicated to keeping zones open.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storage devicecontroller 119. In one embodiment, storage device controller 119A-D maybe a CPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120 a-n includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the stored energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example storage system 124 for data storagein accordance with some implementations. In one embodiment, storagesystem 124 includes storage controllers 125 a, 125 b. In one embodiment,storage controllers 125 a, 125 b are operatively coupled to Dual PCIstorage devices. Storage controllers 125 a, 125 b may be operativelycoupled (e.g., via a storage network 130) to some number of hostcomputers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, storage controllers 125 a, 125 b operate as PCImasters to one or the other PCI buses 128 a, 128 b. In anotherembodiment, 128 a and 128 b may be based on other communicationsstandards (e.g., HyperTransport, InfiniBand, etc.). Other storage systemembodiments may operate storage controllers 125 a, 125 b asmulti-masters for both PCI buses 128 a, 128 b. Alternately, aPCI/NVMe/NVMf switching infrastructure or fabric may connect multiplestorage controllers. Some storage system embodiments may allow storagedevices to communicate with each other directly rather thancommunicating only with storage controllers. In one embodiment, astorage device controller 119 a may be operable under direction from astorage controller 125 a to synthesize and transfer data to be storedinto Flash memory devices from data that has been stored in RAM (e.g.,RAM 121 of FIG. 1C). For example, a recalculated version of RAM contentmay be transferred after a storage controller has determined that anoperation has fully committed across the storage system, or whenfast-write memory on the device has reached a certain used capacity, orafter a certain amount of time, to ensure improve safety of the data orto release addressable fast-write capacity for reuse. This mechanism maybe used, for example, to avoid a second transfer over a bus (e.g., 128a, 128 b) from the storage controllers 125 a, 125 b. In one embodiment,a recalculation may include compressing data, attaching indexing orother metadata, combining multiple data segments together, performingerasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one storage controller 125 a to another storage controller 125b, or it could be used to offload compression, data aggregation, and/orerasure coding calculations and transfers to storage devices to reduceload on storage controllers or the storage controller interface 129 a,129 b to the PCI bus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storage152 units or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storage 152, for exampleas lists or other data structures stored in memory. In some embodimentsthe authorities are stored within the non-volatile solid state storage152 and supported by software executing on a controller or otherprocessor of the non-volatile solid state storage 152. In a furtherembodiment, authorities 168 are implemented on the storage nodes 150,for example as lists or other data structures stored in the memory 154and supported by software executing on the CPU 156 of the storage node150. Authorities 168 control how and where data is stored in thenon-volatile solid state storage 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storage 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In embodiments, authorities 168 operate to determine how operations willproceed against particular logical elements. Each of the logicalelements may be operated on through a particular authority across aplurality of storage controllers of a storage system. The authorities168 may communicate with the plurality of storage controllers so thatthe plurality of storage controllers collectively perform operationsagainst those particular logical elements.

In embodiments, logical elements could be, for example, files,directories, object buckets, individual objects, delineated parts offiles or objects, other forms of key-value pair databases, or tables. Inembodiments, performing an operation can involve, for example, ensuringconsistency, structural integrity, and/or recoverability with otheroperations against the same logical element, reading metadata and dataassociated with that logical element, determining what data should bewritten durably into the storage system to persist any changes for theoperation, or where metadata and data can be determined to be storedacross modular storage devices attached to a plurality of the storagecontrollers in the storage system.

In some embodiments the operations are token based transactions toefficiently communicate within a distributed system. Each transactionmay be accompanied by or associated with a token, which gives permissionto execute the transaction. The authorities 168 are able to maintain apre-transaction state of the system until completion of the operation insome embodiments. The token based communication may be accomplishedwithout a global lock across the system, and also enables restart of anoperation in case of a disruption or other failure.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. modes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage 152 unit may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.,multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The non-volatile solid statestorage 152 units described herein have multiple interfaces activesimultaneously and serving multiple purposes. In some embodiments, someof the functionality of a storage node 150 is shifted into a storageunit 152, transforming the storage unit 152 into a combination ofstorage unit 152 and storage node 150. Placing computing (relative tostorage data) into the storage unit 152 places this computing closer tothe data itself. The various system embodiments have a hierarchy ofstorage node layers with different capabilities. By contrast, in astorage array, a controller owns and knows everything about all of thedata that the controller manages in a shelf or storage devices. In astorage cluster 161, as described herein, multiple controllers inmultiple non-volatile sold state storage 152 units and/or storage nodes150 cooperate in various ways (e.g., for erasure coding, data sharding,metadata communication and redundancy, storage capacity expansion orcontraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage 152 units of FIGS. 2A-C. In thisversion, each non-volatile solid state storage 152 unit has a processorsuch as controller 212 (see FIG. 2C), an FPGA, flash memory 206, andNVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS. 2B and2C) on a PCIe (peripheral component interconnect express) board in achassis 138 (see FIG. 2A). The non-volatile solid state storage 152 unitmay be implemented as a single board containing storage, and may be thelargest tolerable failure domain inside the chassis. In someembodiments, up to two non-volatile solid state storage 152 units mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the non-volatile solid state storage 152 DRAM 216,and is backed by NAND flash. NVRAM 204 is logically divided intomultiple memory regions written for two as spool (e.g., spool_region).Space within the NVRAM 204 spools is managed by each authority 168independently. Each device provides an amount of storage space to eachauthority 168. That authority 168 further manages lifetimes andallocations within that space. Examples of a spool include distributedtransactions or notions. When the primary power to a non-volatile solidstate storage 152 unit fails, onboard super-capacitors provide a shortduration of power hold up. During this holdup interval, the contents ofthe NVRAM 204 are flushed to flash memory 206. On the next power-on, thecontents of the NVRAM 204 are recovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g., partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Sucha data communications link 304 may be fully wired, fully wireless, orsome aggregation of wired and wireless data communications pathways. Insuch an example, digital information may be exchanged between thestorage system 306 and the cloud services provider 302 via the datacommunications link 304 using one or more data communications protocols.For example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using the handheld device transfer protocol(‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol(‘IP’), real-time transfer protocol (‘RTP’), transmission controlprotocol (‘TCP’), user datagram protocol (‘UDP’), wireless applicationprotocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage system 306 and remote, cloud-based storage thatis utilized by the storage system 306. Through the use of a cloudstorage gateway, organizations may move primary iSCSI or NAS to thecloud services provider 302, thereby enabling the organization to savespace on their on-premises storage systems. Such a cloud storage gatewaymay be configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model. For example, the cloud services provider 302may be configured to provide access to data analytics applications tothe storage system 306 and users of the storage system 306. Such dataanalytics applications may be configured, for example, to receive vastamounts of telemetry data phoned home by the storage system 306. Suchtelemetry data may describe various operating characteristics of thestorage system 306 and may be analyzed for a vast array of purposesincluding, for example, to determine the health of the storage system306, to identify workloads that are executing on the storage system 306,to predict when the storage system 306 will run out of variousresources, to recommend configuration changes, hardware or softwareupgrades, workflow migrations, or other actions that may improve theoperation of the storage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

Although the example depicted in FIG. 3A illustrates the storage system306 being coupled for data communications with the cloud servicesprovider 302, in other embodiments the storage system 306 may be part ofa hybrid cloud deployment in which private cloud elements (e.g., privatecloud services, on-premises infrastructure, and so on) and public cloudelements (e.g., public cloud services, infrastructure, and so on thatmay be provided by one or more cloud services providers) are combined toform a single solution, with orchestration among the various platforms.Such a hybrid cloud deployment may leverage hybrid cloud managementsoftware such as, for example, Azure™ Arc from Microsoft™, thatcentralize the management of the hybrid cloud deployment to anyinfrastructure and enable the deployment of services anywhere. In suchan example, the hybrid cloud management software may be configured tocreate, update, and delete resources (both physical and virtual) thatform the hybrid cloud deployment, to allocate compute and storage tospecific workloads, to monitor workloads and resources for performance,policy compliance, updates and patches, security status, or to perform avariety of other tasks.

Readers will appreciate that by pairing the storage systems describedherein with one or more cloud services providers, various offerings maybe enabled. For example, disaster recovery as a service (‘DRaaS’) may beprovided where cloud resources are utilized to protect applications anddata from disruption caused by disaster, including in embodiments wherethe storage systems may serve as the primary data store. In suchembodiments, a total system backup may be taken that allows for businesscontinuity in the event of system failure. In such embodiments, clouddata backup techniques (by themselves or as part of a larger DRaaSsolution) may also be integrated into an overall solution that includesthe storage systems and cloud services providers described herein.

The storage systems described herein, as well as the cloud servicesproviders, may be utilized to provide a wide array of security features.For example, the storage systems may encrypt data at rest (and data maybe sent to and from the storage systems encrypted) and may make use ofKey Management-as-a-Service (‘KMaaS’) to manage encryption keys, keysfor locking and unlocking storage devices, and so on. Likewise, clouddata security gateways or similar mechanisms may be utilized to ensurethat data stored within the storage systems does not improperly end upbeing stored in the cloud as part of a cloud data backup operation.Furthermore, microsegmentation or identity-based-segmentation may beutilized in a data center that includes the storage systems or withinthe cloud services provider, to create secure zones in data centers andcloud deployments that enables the isolation of workloads from oneanother.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘QLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (STY) MRAM. The example storage resources308 may alternatively include non-volatile phase-change memory (‘PCM’),quantum memory that allows for the storage and retrieval of photonicquantum information, resistive random-access memory (‘ReRAM’), storageclass memory (‘SCM’), or other form of storage resources, including anycombination of resources described herein. Readers will appreciate thatother forms of computer memories and storage devices may be utilized bythe storage systems described above, including DRAM, SRAM, EEPROM,universal memory, and many others. The storage resources 308 depicted inFIG. 3A may be embodied in a variety of form factors, including but notlimited to, dual in-line memory modules (‘DIMMs’), non-volatile dualin-line memory modules (‘NVDIMMs’), M.2, U.2, and others.

The storage resources 308 depicted in FIG. 3B may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The storage resources 308 depicted in FIG. 3B may also include racetrackmemory (also referred to as domain-wall memory). Such racetrack memorymay be embodied as a form of non-volatile, solid-state memory thatrelies on the intrinsic strength and orientation of the magnetic fieldcreated by an electron as it spins in addition to its electronic charge,in solid-state devices. Through the use of spin-coherent electriccurrent to move magnetic domains along a nanoscopic permalloy wire, thedomains may pass by magnetic read/write heads positioned near the wireas current is passed through the wire, which alter the domains to recordpatterns of bits. In order to create a racetrack memory device, manysuch wires and read/write elements may be packaged together.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The example storage system 306 depicted in FIG. 3B may leverage thestorage resources described above in a variety of different ways. Forexample, some portion of the storage resources may be utilized to serveas a write cache, storage resources within the storage system may beutilized as a read cache, or tiering may be achieved within the storagesystems by placing data within the storage system in accordance with oneor more tiering policies.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, internet small computersystems interface (‘iSCSI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘SoCs’), or other formof processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques. Such data protection techniques may be carriedout, for example, by system software executing on computer hardwarewithin the storage system, by a cloud services provider, or in otherways. Such data protection techniques can include data archiving, databackup, data replication, data snapshotting, data and database cloning,and other data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage system 306. For example, the software resources 314 may includesoftware modules that perform various data reduction techniques such as,for example, data compression, data deduplication, and others. Thesoftware resources 314 may include software modules that intelligentlygroup together I/O operations to facilitate better usage of theunderlying storage resource 308, software modules that perform datamigration operations to migrate from within a storage system, as well assoftware modules that perform other functions. Such software resources314 may be embodied as one or more software containers or in many otherways.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’)™, Microsoft Azure™, GoogleCloud Platform™, IBM Cloud™, Oracle Cloud™, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. For example, each of the cloud computing instances 320, 322 mayexecute on an Azure VM, where each Azure VM may include high speedtemporary storage that may be leveraged as a cache (e.g., as a readcache). In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (‘AMI’) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data to the cloud-based storage system 318,erasing data from the cloud-based storage system 318, retrieving datafrom the cloud-based storage system 318, monitoring and reporting ofstorage device utilization and performance, performing redundancyoperations, such as RAID or RAID-like data redundancy operations,compressing data, encrypting data, deduplicating data, and so forth.Readers will appreciate that because there are two cloud computinginstances 320, 322 that each include the storage controller application324, 326, in some embodiments one cloud computing instance 320 mayoperate as the primary controller as described above while the othercloud computing instance 322 may operate as the secondary controller asdescribed above. Readers will appreciate that the storage controllerapplication 324, 326 depicted in FIG. 3C may include identical sourcecode that is executed within different cloud computing instances 320,322 such as distinct EC2 instances.

Readers will appreciate that other embodiments that do not include aprimary and secondary controller are within the scope of the presentdisclosure. For example, each cloud computing instance 320, 322 mayoperate as a primary controller for some portion of the address spacesupported by the cloud-based storage system 318, each cloud computinginstance 320, 322 may operate as a primary controller where theservicing of I/O operations directed to the cloud-based storage system318 are divided in some other way, and so on. In fact, in otherembodiments where costs savings may be prioritized over performancedemands, only a single cloud computing instance may exist that containsthe storage controller application.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n may be embodied,for example, as instances of cloud computing resources that may beprovided by the cloud computing environment 316 to support the executionof software applications. The cloud computing instances 340 a, 340 b,340 n of FIG. 3C may differ from the cloud computing instances 320, 322described above as the cloud computing instances 340 a, 340 b, 340 n ofFIG. 3C have local storage 330, 334, 338 resources whereas the cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 need not have local storage resources.The cloud computing instances 340 a, 340 b, 340 n with local storage330, 334, 338 may be embodied, for example, as EC2 M5 instances thatinclude one or more SSDs, as EC2 R5 instances that include one or moreSSDs, as EC2 I3 instances that include one or more SSDs, and so on. Insome embodiments, the local storage 330, 334, 338 must be embodied assolid-state storage (e.g., SSDs) rather than storage that makes use ofhard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block storage 342, 344, 346 that is offered by the cloudcomputing environment 316 such as, for example, as Amazon Elastic BlockStore (‘EBS’) volumes. In such an example, the block storage 342, 344,346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM. In yet another embodiment, high performanceblock storage resources such as one or more Azure Ultra Disks may beutilized as the NVRAM.

When a request to write data is received by a particular cloud computinginstance 340 a, 340 b, 340 n with local storage 330, 334, 338, thesoftware daemon 328, 332, 336 may be configured to not only write thedata to its own local storage 330, 334, 338 resources and anyappropriate block storage 342, 344, 346 resources, but the softwaredaemon 328, 332, 336 may also be configured to write the data tocloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n. The cloud-based object storage348 that is attached to the particular cloud computing instance 340 a,340 b, 340 n may be embodied, for example, as Amazon Simple StorageService (‘S3’). In other embodiments, the cloud computing instances 320,322 that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the cloud-basedobject storage 348. In other embodiments, rather than using both thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 (also referred to herein as ‘virtual drives’) and thecloud-based object storage 348 to store data, a persistent storage layermay be implemented in other ways. For example, one or more Azure Ultradisks may be used to persistently store data (e.g., after the data hasbeen written to the NVRAM layer). In an embodiment where one or moreAzure Ultra disks may be used to persistently store data, the usage of acloud-based object storage 348 may be eliminated such that data is onlystored persistently in the Azure Ultra disks without also writing thedata to an object storage layer.

While the local storage 330, 334, 338 resources and the block storage342, 344, 346 resources that are utilized by the cloud computinginstances 340 a, 340 b, 340 n may support block-level access, thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n supports only object-basedaccess. The software daemon 328, 332, 336 may therefore be configured totake blocks of data, package those blocks into objects, and write theobjects to the cloud-based object storage 348 that is attached to theparticular cloud computing instance 340 a, 340 b, 340 n.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n.

One or more modules of computer program instructions that are executingwithin the cloud-based storage system 318 (e.g., a monitoring modulethat is executing on its own EC2 instance) may be designed to handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338. In such an example, themonitoring module may handle the failure of one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334, 338by creating one or more new cloud computing instances with localstorage, retrieving data that was stored on the failed cloud computinginstances 340 a, 340 b, 340 n from the cloud-based object storage 348,and storing the data retrieved from the cloud-based object storage 348in local storage on the newly created cloud computing instances. Readerswill appreciate that many variants of this process may be implemented.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Forexample, if the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318, a monitoringmodule may create a new, more powerful cloud computing instance (e.g., acloud computing instance of a type that includes more processing power,more memory, etc. . . . ) that includes the storage controllerapplication such that the new, more powerful cloud computing instancecan begin operating as the primary controller. Likewise, if themonitoring module determines that the cloud computing instances 320, 322that are used to support the execution of a storage controllerapplication 324, 326 are oversized and that cost savings could be gainedby switching to a smaller, less powerful cloud computing instance, themonitoring module may create a new, less powerful (and less expensive)cloud computing instance that includes the storage controllerapplication such that the new, less powerful cloud computing instancecan begin operating as the primary controller.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described in thisdisclosure may be useful for supporting various types of softwareapplications. In fact, the storage systems may be ‘application aware’ inthe sense that the storage systems may obtain, maintain, or otherwisehave access to information describing connected applications (e.g.,applications that utilize the storage systems) to optimize the operationof the storage system based on intelligence about the applications andtheir utilization patterns. For example, the storage system may optimizedata layouts, optimize caching behaviors, optimize ‘QoS’ levels, orperform some other optimization that is designed to improve the storageperformance that is experienced by the application.

As an example of one type of application that may be supported by thestorage systems describe herein, the storage system 306 may be useful insupporting artificial intelligence (‘AI’) applications, databaseapplications, XOps projects (e.g., DevOps projects, DataOps projects,MLOps projects, ModelOps projects, PlatformOps projects), electronicdesign automation tools, event-driven software applications, highperformance computing applications, simulation applications, high-speeddata capture and analysis applications, machine learning applications,media production applications, media serving applications, picturearchiving and communication systems (‘PACS’) applications, softwaredevelopment applications, virtual reality applications, augmentedreality applications, and many other types of applications by providingstorage resources to such applications.

In view of the fact that the storage systems include compute resources,storage resources, and a wide variety of other resources, the storagesystems may be well suited to support applications that are resourceintensive such as, for example, AI applications. AI applications may bedeployed in a variety of fields, including: predictive maintenance inmanufacturing and related fields, healthcare applications such aspatient data & risk analytics, retail and marketing deployments (e.g.,search advertising, social media advertising), supply chains solutions,fintech solutions such as business analytics & reporting tools,operational deployments such as real-time analytics tools, applicationperformance management tools, IT infrastructure management tools, andmany others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson™, MicrosoftOxford™, Google DeepMind™, Baidu Minwa™, and others.

The storage systems described above may also be well suited to supportother types of applications that are resource intensive such as, forexample, machine learning applications. Machine learning applicationsmay perform various types of data analysis to automate analytical modelbuilding. Using algorithms that iteratively learn from data, machinelearning applications can enable computers to learn without beingexplicitly programmed. One particular area of machine learning isreferred to as reinforcement learning, which involves taking suitableactions to maximize reward in a particular situation.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks, including the development ofmulti-layer neural networks, have ignited a new wave of algorithms andtools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of AI techniques have materialized in a wide arrayof products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

In order for the storage systems described above to serve as a data hubor as part of an AI deployment, in some embodiments the storage systemsmay be configured to provide DMA between storage devices that areincluded in the storage systems and one or more GPUs that are used in anAI or big data analytics pipeline. The one or more GPUs may be coupledto the storage system, for example, via NVMe-over-Fabrics (‘NVMe-oF’)such that bottlenecks such as the host CPU can be bypassed and thestorage system (or one of the components contained therein) can directlyaccess GPU memory. In such an example, the storage systems may leverageAPI hooks to the GPUs to transfer data directly to the GPUs. Forexample, the GPUs may be embodied as Nvidia™ GPUs and the storagesystems may support GPUDirect Storage (‘GDS’) software, or have similarproprietary software, that enables the storage system to transfer datato the GPUs via RDMA or similar mechanism.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains and derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics, including being leveraged as part of a composable dataanalytics pipeline where containerized analytics architectures, forexample, make analytics capabilities more composable. Big data analyticsmay be generally described as the process of examining large and varieddata sets to uncover hidden patterns, unknown correlations, markettrends, customer preferences and other useful information that can helporganizations make more-informed business decisions. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa™, Apple Siri™, Google Voice™, Samsung Bixby™,Microsoft Cortana™, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain-computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

The storage systems described above may also be configured to implementNVMe Zoned Namespaces. Through the use of NVMe Zoned Namespaces, thelogical address space of a namespace is divided into zones. Each zoneprovides a logical block address range that must be written sequentiallyand explicitly reset before rewriting, thereby enabling the creation ofnamespaces that expose the natural boundaries of the device and offloadmanagement of internal mapping tables to the host. In order to implementNVMe Zoned Name Spaces (‘ZNS’), ZNS SSDs or some other form of zonedblock devices may be utilized that expose a namespace logical addressspace using zones. With the zones aligned to the internal physicalproperties of the device, several inefficiencies in the placement ofdata can be eliminated. In such embodiments, each zone may be mapped,for example, to a separate application such that functions like wearlevelling and garbage collection could be performed on a per-zone orper-application basis rather than across the entire device. In order tosupport ZNS, the storage controllers described herein may be configuredwith to interact with zoned block devices through the usage of, forexample, the Linux™ kernel zoned block device interface or other tools.

The storage systems described above may also be configured to implementzoned storage in other ways such as, for example, through the usage ofshingled magnetic recording (SMR) storage devices. In examples wherezoned storage is used, device-managed embodiments may be deployed wherethe storage devices hide this complexity by managing it in the firmware,presenting an interface like any other storage device. Alternatively,zoned storage may be implemented via a host-managed embodiment thatdepends on the operating system to know how to handle the drive, andonly write sequentially to certain regions of the drive. Zoned storagemay similarly be implemented using a host-aware embodiment in which acombination of a drive managed and host managed implementation isdeployed.

The storage systems described herein may be used to form a data lake. Adata lake may operate as the first place that an organization's dataflows to, where such data may be in a raw format. Metadata tagging maybe implemented to facilitate searches of data elements in the data lake,especially in embodiments where the data lake contains multiple storesof data, in formats not easily accessible or readable (e.g.,unstructured data, semi-structured data, structured data). From the datalake, data may go downstream to a data warehouse where data may bestored in a more processed, packaged, and consumable format. The storagesystems described above may also be used to implement such a datawarehouse. In addition, a data mart or data hub may allow for data thatis even more easily consumed, where the storage systems described abovemay also be used to provide the underlying storage resources necessaryfor a data mart or data hub. In embodiments, queries the data lake mayrequire a schema-on-read approach, where data is applied to a plan orschema as it is pulled out of a stored location, rather than as it goesinto the stored location.

The storage systems described herein may also be configured implement arecovery point objective (‘RPO’), which may be establish by a user,established by an administrator, established as a system default,established as part of a storage class or service that the storagesystem is participating in the delivery of, or in some other way. A“recovery point objective” is a goal for the maximum time differencebetween the last update to a source dataset and the last recoverablereplicated dataset update that would be correctly recoverable, given areason to do so, from a continuously or frequently updated copy of thesource dataset. An update is correctly recoverable if it properly takesinto account all updates that were processed on the source dataset priorto the last recoverable replicated dataset update.

In synchronous replication, the RPO would be zero, meaning that undernormal operation, all completed updates on the source dataset should bepresent and correctly recoverable on the copy dataset. In best effortnearly synchronous replication, the RPO can be as low as a few seconds.In snapshot-based replication, the RPO can be roughly calculated as theinterval between snapshots plus the time to transfer the modificationsbetween a previous already transferred snapshot and the most recentto-be-replicated snapshot.

If updates accumulate faster than they are replicated, then an RPO canbe missed. If more data to be replicated accumulates between twosnapshots, for snapshot-based replication, than can be replicatedbetween taking the snapshot and replicating that snapshot's cumulativeupdates to the copy, then the RPO can be missed. If, again insnapshot-based replication, data to be replicated accumulates at afaster rate than could be transferred in the time between subsequentsnapshots, then replication can start to fall further behind which canextend the miss between the expected recovery point objective and theactual recovery point that is represented by the last correctlyreplicated update.

The storage systems described above may also be part of a shared nothingstorage cluster. In a shared nothing storage cluster, each node of thecluster has local storage and communicates with other nodes in thecluster through networks, where the storage used by the cluster is (ingeneral) provided only by the storage connected to each individual node.A collection of nodes that are synchronously replicating a dataset maybe one example of a shared nothing storage cluster, as each storagesystem has local storage and communicates to other storage systemsthrough a network, where those storage systems do not (in general) usestorage from somewhere else that they share access to through some kindof interconnect. In contrast, some of the storage systems describedabove are themselves built as a shared-storage cluster, since there aredrive shelves that are shared by the paired controllers. Other storagesystems described above, however, are built as a shared nothing storagecluster, as all storage is local to a particular node (e.g., a blade)and all communication is through networks that link the compute nodestogether.

In other embodiments, other forms of a shared nothing storage clustercan include embodiments where any node in the cluster has a local copyof all storage they need, and where data is mirrored through asynchronous style of replication to other nodes in the cluster either toensure that the data isn't lost or because other nodes are also usingthat storage. In such an embodiment, if a new cluster node needs somedata, that data can be copied to the new node from other nodes that havecopies of the data.

In some embodiments, mirror-copy-based shared storage clusters may storemultiple copies of all the cluster's stored data, with each subset ofdata replicated to a particular set of nodes, and different subsets ofdata replicated to different sets of nodes. In some variations,embodiments may store all of the cluster's stored data in all nodes,whereas in other variations nodes may be divided up such that a firstset of nodes will all store the same set of data and a second, differentset of nodes will all store a different set of data.

Readers will appreciate that RAFT-based databases (e.g., etcd) mayoperate like shared-nothing storage clusters where all RAFT nodes storeall data. The amount of data stored in a RAFT cluster, however, may belimited so that extra copies don't consume too much storage. A containerserver cluster might also be able to replicate all data to all clusternodes, presuming the containers don't tend to be too large and theirbulk data (the data manipulated by the applications that run in thecontainers) is stored elsewhere such as in an S3 cluster or an externalfile server. In such an example, the container storage may be providedby the cluster directly through its shared-nothing storage model, withthose containers providing the images that form the executionenvironment for parts of an application or service.

For further explanation, FIG. 3D illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3D, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3D, the components illustrated in FIG. 3D are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3D willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

For further explanation, FIG. 3E illustrates an example of a fleet ofstorage systems 376 for providing storage services (also referred toherein as ‘data services’). The fleet of storage systems 376 depicted inFIG. 3 includes a plurality of storage systems 374 a, 374 b, 374 c, 374d, 374 n that may each be similar to the storage systems describedherein. The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in thefleet of storage systems 376 may be embodied as identical storagesystems or as different types of storage systems. For example, two ofthe storage systems 374 a, 374 n depicted in FIG. 3E are depicted asbeing cloud-based storage systems, as the resources that collectivelyform each of the storage systems 374 a, 374 n are provided by distinctcloud services providers 370, 372. For example, the first cloud servicesprovider 370 may be Amazon AWS™ whereas the second cloud servicesprovider 372 is Microsoft Azure™, although in other embodiments one ormore public clouds, private clouds, or combinations thereof may be usedto provide the underlying resources that are used to form a particularstorage system in the fleet of storage systems 376.

The example depicted in FIG. 3E includes an edge management service 366for delivering storage services in accordance with some embodiments ofthe present disclosure. The storage services (also referred to herein as‘data services’) that are delivered may include, for example, servicesto provide a certain amount of storage to a consumer, services toprovide storage to a consumer in accordance with a predetermined servicelevel agreement, services to provide storage to a consumer in accordancewith predetermined regulatory requirements, and many others.

The edge management service 366 depicted in FIG. 3E may be embodied, forexample, as one or more modules of computer program instructionsexecuting on computer hardware such as one or more computer processors.Alternatively, the edge management service 366 may be embodied as one ormore modules of computer program instructions executing on a virtualizedexecution environment such as one or more virtual machines, in one ormore containers, or in some other way. In other embodiments, the edgemanagement service 366 may be embodied as a combination of theembodiments described above, including embodiments where the one or moremodules of computer program instructions that are included in the edgemanagement service 366 are distributed across multiple physical orvirtual execution environments.

The edge management service 366 may operate as a gateway for providingstorage services to storage consumers, where the storage servicesleverage storage offered by one or more storage systems 374 a, 374 b,374 c, 374 d, 374 n. For example, the edge management service 366 may beconfigured to provide storage services to host devices 378 a, 378 b, 378c, 378 d, 378 n that are executing one or more applications that consumethe storage services. In such an example, the edge management service366 may operate as a gateway between the host devices 378 a, 378 b, 378c, 378 d, 378 n and the storage systems 374 a, 374 b, 374 c, 374 d, 374n, rather than requiring that the host devices 378 a, 378 b, 378 c, 378d, 378 n directly access the storage systems 374 a, 374 b, 374 c, 374 d,374 n.

The edge management service 366 of FIG. 3E exposes a storage servicesmodule 364 to the host devices 378 a, 378 b, 378 c, 378 d, 378 n of FIG.3E, although in other embodiments the edge management service 366 mayexpose the storage services module 364 to other consumers of the variousstorage services. The various storage services may be presented toconsumers via one or more user interfaces, via one or more APIs, orthrough some other mechanism provided by the storage services module364. As such, the storage services module 364 depicted in FIG. 3E may beembodied as one or more modules of computer program instructionsexecuting on physical hardware, on a virtualized execution environment,or combinations thereof, where executing such modules causes enables aconsumer of storage services to be offered, select, and access thevarious storage services.

The edge management service 366 of FIG. 3E also includes a systemmanagement services module 368. The system management services module368 of FIG. 3E includes one or more modules of computer programinstructions that, when executed, perform various operations incoordination with the storage systems 374 a, 374 b, 374 c, 374 d, 374 nto provide storage services to the host devices 378 a, 378 b, 378 c, 378d, 378 n. The system management services module 368 may be configured,for example, to perform tasks such as provisioning storage resourcesfrom the storage systems 374 a, 374 b, 374 c, 374 d, 374 n via one ormore APIs exposed by the storage systems 374 a, 374 b, 374 c, 374 d, 374n, migrating datasets or workloads amongst the storage systems 374 a,374 b, 374 c, 374 d, 374 n via one or more APIs exposed by the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n, setting one or more tunableparameters (i.e., one or more configurable settings) on the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n via one or more APIs exposedby the storage systems 374 a, 374 b, 374 c, 374 d, 374 n, and so on. Forexample, many of the services described below relate to embodimentswhere the storage systems 374 a, 374 b, 374 c, 374 d, 374 n areconfigured to operate in some way. In such examples, the systemmanagement services module 368 may be responsible for using APIs (orsome other mechanism) provided by the storage systems 374 a, 374 b, 374c, 374 d, 374 n to configure the storage systems 374 a, 374 b, 374 c,374 d, 374 n to operate in the ways described below.

In addition to configuring the storage systems 374 a, 374 b, 374 c, 374d, 374 n, the edge management service 366 itself may be configured toperform various tasks required to provide the various storage services.Consider an example in which the storage service includes a servicethat, when selected and applied, causes personally identifiableinformation (‘PII’) contained in a dataset to be obfuscated when thedataset is accessed. In such an example, the storage systems 374 a, 374b, 374 c, 374 d, 374 n may be configured to obfuscate PII when servicingread requests directed to the dataset. Alternatively, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may service reads by returningdata that includes the PII, but the edge management service 366 itselfmay obfuscate the PII as the data is passed through the edge managementservice 366 on its way from the storage systems 374 a, 374 b, 374 c, 374d, 374 n to the host devices 378 a, 378 b, 378 c, 378 d, 378 n.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may be embodied as one or more of the storage systems described abovewith reference to FIGS. 1A-3D, including variations thereof. In fact,the storage systems 374 a, 374 b, 374 c, 374 d, 374 n may serve as apool of storage resources where the individual components in that poolhave different performance characteristics, different storagecharacteristics, and so on. For example, one of the storage systems 374a may be a cloud-based storage system, another storage system 374 b maybe a storage system that provides block storage, another storage system374 c may be a storage system that provides file storage, anotherstorage system 374 d may be a relatively high-performance storage systemwhile another storage system 374 n may be a relatively low-performancestorage system, and so on. In alternative embodiments, only a singlestorage system may be present.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may also be organized into different failure domains so that thefailure of one storage system 374 a should be totally unrelated to thefailure of another storage system 374 b. For example, each of thestorage systems may receive power from independent power systems, eachof the storage systems may be coupled for data communications overindependent data communications networks, and so on. Furthermore, thestorage systems in a first failure domain may be accessed via a firstgateway whereas storage systems in a second failure domain may beaccessed via a second gateway. For example, the first gateway may be afirst instance of the edge management service 366 and the second gatewaymay be a second instance of the edge management service 366, includingembodiments where each instance is distinct, or each instance is part ofa distributed edge management service 366.

As an illustrative example of available storage services, storageservices may be presented to a user that are associated with differentlevels of data protection. For example, storage services may bepresented to the user that, when selected and enforced, guarantee theuser that data associated with that user will be protected such thatvarious recovery point objectives (‘RPO’) can be guaranteed. A firstavailable storage service may ensure, for example, that some datasetassociated with the user will be protected such that any data that ismore than 5 seconds old can be recovered in the event of a failure ofthe primary data store whereas a second available storage service mayensure that the dataset that is associated with the user will beprotected such that any data that is more than 5 minutes old can berecovered in the event of a failure of the primary data store.

An additional example of storage services that may be presented to auser, selected by a user, and ultimately applied to a dataset associatedwith the user can include one or more data compliance services. Suchdata compliance services may be embodied, for example, as services thatmay be provided to consumers (i.e., a user) the data compliance servicesto ensure that the user's datasets are managed in a way to adhere tovarious regulatory requirements. For example, one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to the General DataProtection Regulation (‘GDPR’), one or data compliance services may beoffered to a user to ensure that the user's datasets are managed in away so as to adhere to the Sarbanes-Oxley Act of 2002 (′ SOX′), or oneor more data compliance services may be offered to a user to ensure thatthe user's datasets are managed in a way so as to adhere to some otherregulatory act. In addition, the one or more data compliance servicesmay be offered to a user to ensure that the user's datasets are managedin a way so as to adhere to some non-governmental guidance (e.g., toadhere to best practices for auditing purposes), the one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to a particular clients ororganizations requirements, and so on.

In order to provide this particular data compliance service, the datacompliance service may be presented to a user (e.g., via a GUI) andselected by the user. In response to receiving the selection of theparticular data compliance service, one or more storage servicespolicies may be applied to a dataset associated with the user to carryout the particular data compliance service. For example, a storageservices policy may be applied requiring that the dataset be encryptedprior to be stored in a storage system, prior to being stored in a cloudenvironment, or prior to being stored elsewhere. In order to enforcethis policy, a requirement may be enforced not only requiring that thedataset be encrypted when stored, but a requirement may be put in placerequiring that the dataset be encrypted prior to transmitting thedataset (e.g., sending the dataset to another party). In such anexample, a storage services policy may also be put in place requiringthat any encryption keys used to encrypt the dataset are not stored onthe same system that stores the dataset itself. Readers will appreciatethat many other forms of data compliance services may be offered andimplemented in accordance with embodiments of the present disclosure.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in the fleet ofstorage systems 376 may be managed collectively, for example, by one ormore fleet management modules. The fleet management modules may be partof or separate from the system management services module 368 depictedin FIG. 3E. The fleet management modules may perform tasks such asmonitoring the health of each storage system in the fleet, initiatingupdates or upgrades on one or more storage systems in the fleet,migrating workloads for loading balancing or other performance purposes,and many other tasks. As such, and for many other reasons, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may be coupled to each othervia one or more data communications links in order to exchange databetween the storage systems 374 a, 374 b, 374 c, 374 d, 374 n.

In some embodiments, one or more storage systems or one or more elementsof storage systems (e.g., features, services, operations, components,etc. of storage systems), such as any of the illustrative storagesystems or storage system elements described herein may be implementedin one or more container systems. A container system may include anysystem that supports execution of one or more containerized applicationsor services. Such a service may be software deployed as infrastructurefor building applications, for operating a run-time environment, and/oras infrastructure for other services. In the discussion that follows,descriptions of containerized applications generally apply tocontainerized services as well.

A container may combine one or more elements of a containerized softwareapplication together with a runtime environment for operating thoseelements of the software application bundled into a single image. Forexample, each such container of a containerized application may includeexecutable code of the software application and various dependencies,libraries, and/or other components, together with network configurationsand configured access to additional resources, used by the elements ofthe software application within the particular container in order toenable operation of those elements. A containerized application can berepresented as a collection of such containers that together representall the elements of the application combined with the various run-timeenvironments needed for all those elements to run. As a result, thecontainerized application may be abstracted away from host operatingsystems as a combined collection of lightweight and portable packagesand configurations, where the containerized application may be uniformlydeployed and consistently executed in different computing environmentsthat use different container-compatible operating systems or differentinfrastructures. In some embodiments, a containerized application sharesa kernel with a host computer system and executes as an isolatedenvironment (an isolated collection of files and directories, processes,system and network resources, and configured access to additionalresources and capabilities) that is isolated by an operating system of ahost system in conjunction with a container management framework. Whenexecuted, a containerized application may provide one or morecontainerized workloads and/or services.

The container system may include and/or utilize a cluster of nodes. Forexample, the container system may be configured to manage deployment andexecution of containerized applications on one or more nodes in acluster. The containerized applications may utilize resources of thenodes, such as memory, processing and/or storage resources providedand/or accessed by the nodes. The storage resources may include any ofthe illustrative storage resources described herein and may includeon-node resources such as a local tree of files and directories,off-node resources such as external networked file systems, databases orobject stores, or both on-node and off-node resources. Access toadditional resources and capabilities that could be configured forcontainers of a containerized application could include specializedcomputation capabilities such as GPUs and AI/ML engines, or specializedhardware such as sensors and cameras.

In some embodiments, the container system may include a containerorchestration system (which may also be referred to as a containerorchestrator, a container orchestration platform, etc.) designed to makeit reasonably simple and for many use cases automated to deploy, scale,and manage containerized applications. In some embodiments, thecontainer system may include a storage management system configured toprovision and manage storage resources (e.g., virtual volumes) forprivate or shared use by cluster nodes and/or containers ofcontainerized applications.

FIG. 3F illustrates an example container system 380. In this example,the container system 380 includes a container storage system 381 thatmay be configured to perform one or more storage management operationsto organize, provision, and manage storage resources for use by one ormore containerized applications 382-1 through 382-L of container system380. In particular, the container storage system 381 may organizestorage resources into one or more storage pools 383 of storageresources for use by containerized applications 382-1 through 382-L. Thecontainer storage system may itself be implemented as a containerizedservice.

The container system 380 may include or be implemented by one or morecontainer orchestration systems, including Kubernetes™, Mesos™, DockerSwarm™, among others. The container orchestration system may manage thecontainer system 380 running on a cluster 384 through servicesimplemented by a control node, depicted as 385, and may further managethe container storage system or the relationship between individualcontainers and their storage, memory and CPU limits, networking, andtheir access to additional resources or services.

A control plane of the container system 380 may implement services thatinclude: deploying applications via a controller 386, monitoringapplications via the controller 386, providing an interface via an APIserver 387, and scheduling deployments via scheduler 388. In thisexample, controller 386, scheduler 388, API server 387, and containerstorage system 381 are implemented on a single node, node 385. In otherexamples, for resiliency, the control plane may be implemented bymultiple, redundant nodes, where if a node that is providing managementservices for the container system 380 fails, then another, redundantnode may provide management services for the cluster 384.

A data plane of the container system 380 may include a set of nodes thatprovides container runtimes for executing containerized applications. Anindividual node within the cluster 384 may execute a container runtime,such as Docker™, and execute a container manager, or node agent, such asa kubelet in Kubernetes (not depicted) that communicates with thecontrol plane via a local network-connected agent (sometimes called aproxy), such as an agent 389. The agent 389 may route network traffic toand from containers using, for example, Internet Protocol (IP) portnumbers. For example, a containerized application may request a storageclass from the control plane, where the request is handled by thecontainer manager, and the container manager communicates the request tothe control plane using the agent 389.

Cluster 384 may include a set of nodes that run containers for managedcontainerized applications. A node may be a virtual or physical machine.A node may be a host system.

The container storage system 381 may orchestrate storage resources toprovide storage to the container system 380. For example, the containerstorage system 381 may provide persistent storage to containerizedapplications 382-1-382-L using the storage pool 383. The containerstorage system 381 may itself be deployed as a containerized applicationby a container orchestration system.

For example, the container storage system 381 application may bedeployed within cluster 384 and perform management functions forproviding storage to the containerized applications 382. Managementfunctions may include determining one or more storage pools fromavailable storage resources, provisioning virtual volumes on one or morenodes, replicating data, responding to and recovering from host andnetwork faults, or handling storage operations. The storage pool 383 mayinclude storage resources from one or more local or remote sources,where the storage resources may be different types of storage,including, as examples, block storage, file storage, and object storage.

The container storage system 381 may also be deployed on a set of nodesfor which persistent storage may be provided by the containerorchestration system. In some examples, the container storage system 381may be deployed on all nodes in a cluster 384 using, for example, aKubernetes DaemonSet. In this example, nodes 390-1 through 390-N providea container runtime where container storage system 381 executes. Inother examples, some, but not all nodes in a cluster may execute thecontainer storage system 381.

The container storage system 381 may handle storage on a node andcommunicate with the control plane of container system 380, to providedynamic volumes, including persistent volumes. A persistent volume maybe mounted on a node as a virtual volume, such as virtual volumes 391-1and 391-P. After a virtual volume 391 is mounted, containerizedapplications may request and use, or be otherwise configured to use,storage provided by the virtual volume 391. In this example, thecontainer storage system 381 may install a driver on a kernel of a node,where the driver handles storage operations directed to the virtualvolume. In this example, the driver may receive a storage operationdirected to a virtual volume, and in response, the driver may performthe storage operation on one or more storage resources within thestorage pool 383, possibly under direction from or using additionallogic within containers that implement the container storage system 381as a containerized service.

The container storage system 381 may, in response to being deployed as acontainerized service, determine available storage resources. Forexample, storage resources 392-1 through 392-M may include localstorage, remote storage (storage on a separate node in a cluster), orboth local and remote storage. Storage resources may also includestorage from external sources such as various combinations of blockstorage systems, file storage systems, and object storage systems. Thestorage resources 392-1 through 392-M may include any type(s) and/orconfiguration(s) of storage resources (e.g., any of the illustrativestorage resources described above), and the container storage system 381may be configured to determine the available storage resources in anysuitable way, including based on a configuration file. For example, aconfiguration file may specify account and authentication informationfor cloud-based object storage 348 or for a cloud-based storage system318. The container storage system 381 may also determine availability ofone or more storage devices 356 or one or more storage systems. Anaggregate amount of storage from one or more of storage device(s) 356,storage system(s), cloud-based storage system(s) 318, edge managementservices 366, cloud-based object storage 348, or any other storageresources, or any combination or sub-combination of such storageresources may be used to provide the storage pool 383. The storage pool383 is used to provision storage for the one or more virtual volumesmounted on one or more of the nodes 390 within cluster 384.

In some implementations, the container storage system 381 may createmultiple storage pools. For example, the container storage system 381may aggregate storage resources of a same type into an individualstorage pool. In this example, a storage type may be one of: a storagedevice 356, a storage array 102, a cloud-based storage system 318,storage via an edge management service 366, or a cloud-based objectstorage 348. Or it could be storage configured with a certain level ortype of redundancy or distribution, such as a particular combination ofstriping, mirroring, or erasure coding.

The container storage system 381 may execute within the cluster 384 as acontainerized container storage system service, where instances ofcontainers that implement elements of the containerized containerstorage system service may operate on different nodes within the cluster384. In this example, the containerized container storage system servicemay operate in conjunction with the container orchestration system ofthe container system 380 to handle storage operations, mount virtualvolumes to provide storage to a node, aggregate available storage into astorage pool 383, provision storage for a virtual volume from a storagepool 383, generate backup data, replicate data between nodes, clusters,environments, among other storage system operations. In some examples,the containerized container storage system service may provide storageservices across multiple clusters operating in distinct computingenvironments. For example, other storage system operations may includestorage system operations described herein. Persistent storage providedby the containerized container storage system service may be used toimplement stateful and/or resilient containerized applications.

The container storage system 381 may be configured to perform anysuitable storage operations of a storage system. For example, thecontainer storage system 381 may be configured to perform one or more ofthe illustrative storage management operations described herein tomanage storage resources used by the container system.

In some embodiments, one or more storage operations, including one ormore of the illustrative storage management operations described herein,may be containerized. For example, one or more storage operations may beimplemented as one or more containerized applications configured to beexecuted to perform the storage operation(s). Such containerized storageoperations may be executed in any suitable runtime environment to manageany storage system(s), including any of the illustrative storage systemsdescribed herein.

The storage systems described herein may support various forms of datareplication. For example, two or more of the storage systems maysynchronously replicate a dataset between each other. In synchronousreplication, distinct copies of a particular dataset may be maintainedby multiple storage systems, but all accesses (e.g., a read) of thedataset should yield consistent results regardless of which storagesystem the access was directed to. For example, a read directed to anyof the storage systems that are synchronously replicating the datasetshould return identical results. As such, while updates to the versionof the dataset need not occur at exactly the same time, precautions mustbe taken to ensure consistent accesses to the dataset. For example, ifan update (e.g., a write) that is directed to the dataset is received bya first storage system, the update may only be acknowledged as beingcompleted if all storage systems that are synchronously replicating thedataset have applied the update to their copies of the dataset. In suchan example, synchronous replication may be carried out through the useof I/O forwarding (e.g., a write received at a first storage system isforwarded to a second storage system), communications between thestorage systems (e.g., each storage system indicating that it hascompleted the update), or in other ways.

In other embodiments, a dataset may be replicated through the use ofcheckpoints. In checkpoint-based replication (also referred to as‘nearly synchronous replication’), a set of updates to a dataset (e.g.,one or more write operations directed to the dataset) may occur betweendifferent checkpoints, such that a dataset has been updated to aspecific checkpoint only if all updates to the dataset prior to thespecific checkpoint have been completed. Consider an example in which afirst storage system stores a live copy of a dataset that is beingaccessed by users of the dataset. In this example, assume that thedataset is being replicated from the first storage system to a secondstorage system using checkpoint-based replication. For example, thefirst storage system may send a first checkpoint (at time t=0) to thesecond storage system, followed by a first set of updates to thedataset, followed by a second checkpoint (at time t=1), followed by asecond set of updates to the dataset, followed by a third checkpoint (attime t=2). In such an example, if the second storage system hasperformed all updates in the first set of updates but has not yetperformed all updates in the second set of updates, the copy of thedataset that is stored on the second storage system may be up-to-dateuntil the second checkpoint. Alternatively, if the second storage systemhas performed all updates in both the first set of updates and thesecond set of updates, the copy of the dataset that is stored on thesecond storage system may be up-to-date until the third checkpoint.Readers will appreciate that various types of checkpoints may be used(e.g., metadata only checkpoints), checkpoints may be spread out basedon a variety of factors (e.g., time, number of operations, an RPOsetting), and so on.

In other embodiments, a dataset may be replicated through snapshot-basedreplication (also referred to as ‘asynchronous replication’). Insnapshot-based replication, snapshots of a dataset may be sent from areplication source such as a first storage system to a replicationtarget such as a second storage system. In such an embodiment, eachsnapshot may include the entire dataset or a subset of the dataset suchas, for example, only the portions of the dataset that have changedsince the last snapshot was sent from the replication source to thereplication target. Readers will appreciate that snapshots may be senton-demand, based on a policy that takes a variety of factors intoconsideration (e.g., time, number of operations, an RPO setting), or insome other way.

The storage systems described above may, either alone or in combination,by configured to serve as a continuous data protection store. Acontinuous data protection store is a feature of a storage system thatrecords updates to a dataset in such a way that consistent images ofprior contents of the dataset can be accessed with a low timegranularity (often on the order of seconds, or even less), andstretching back for a reasonable period of time (often hours or days).These allow access to very recent consistent points in time for thedataset, and also allow access to access to points in time for a datasetthat might have just preceded some event that, for example, caused partsof the dataset to be corrupted or otherwise lost, while retaining closeto the maximum number of updates that preceded that event. Conceptually,they are like a sequence of snapshots of a dataset taken very frequentlyand kept for a long period of time, though continuous data protectionstores are often implemented quite differently from snapshots. A storagesystem implementing a data continuous data protection store may furtherprovide a means of accessing these points in time, accessing one or moreof these points in time as snapshots or as cloned copies, or revertingthe dataset back to one of those recorded points in time.

Over time, to reduce overhead, some points in the time held in acontinuous data protection store can be merged with other nearby pointsin time, essentially deleting some of these points in time from thestore. This can reduce the capacity needed to store updates. It may alsobe possible to convert a limited number of these points in time intolonger duration snapshots. For example, such a store might keep a lowgranularity sequence of points in time stretching back a few hours fromthe present, with some points in time merged or deleted to reduceoverhead for up to an additional day. Stretching back in the pastfurther than that, some of these points in time could be converted tosnapshots representing consistent point-in-time images from only everyfew hours.

Although some embodiments are described largely in the context of astorage system, readers of skill in the art will recognize thatembodiments of the present disclosure may also take the form of acomputer program product disposed upon computer readable storage mediafor use with any suitable processing system. Such computer readablestorage media may be any storage medium for machine-readableinformation, including magnetic media, optical media, solid-state media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsdescribed herein as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present disclosure.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g., a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

One or more embodiments may be described herein with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

While particular combinations of various functions and features of theone or more embodiments are expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

For further explanation, FIG. 4 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The method of FIG. 4 may beimplemented by one or more storage systems such as the storage systemsdescribed above with reference to FIGS. 1A-3E. In some embodiments, thesteps described herein may be performed by one or more storagecontrollers that are included within a particular storage system. Inother embodiments, the steps may be performed (at least to some extent)by fleet management software or some other software that is responsiblefor the management of a fleet of storage systems (e.g., two or morestorage systems).

The example method depicted in FIG. 4 includes detecting 402 that aportion of the dataset stored in a first storage system has becomeunavailable. The portion of the dataset stored in a first storage systemmay become unavailable for a variety of reasons. For example, a storagedevice that contains a portion of the dataset may fail, a portion (e.g.,a flash die) of a storage device may fail, a malicious actor may rendersome portion of the dataset as being unavailable, or some other eventmay occur that makes the portion of the dataset unavailable. As such,detecting 402 that a portion of the dataset stored in a first storagesystem has become unavailable may be carried out by detecting theoccurrence of such an event, by receiving an uncorrectable read errorwhen attempting to read the portion of the dataset, or in some otherway.

Readers will appreciate that, as the phrase is used here, a portion ofthe dataset becoming unavailable is distinguishable from a situation inwhich some user (e.g., an application that is executing on a hostdevice) is prevented from accessing the dataset. A portion of thedataset may become unavailable when a system-level entity (e.g., astorage system controller, a controller on a particular storage device)cannot access the portion of the dataset without error. A portion of thedataset is not unavailable, however, just because a user-level entity(e.g., an application executing on a host device) cannot access theportion of the dataset. A user-level entity may not be able to accesssome portion (or the entirety) of the dataset for a variety of reasonsincluding, for example, the user-level entity does not have the requiredpermissions, the portion of the dataset includes personal data that isnot to be shared with the user-level entity, or for some other reason.In contrast to the aforementioned scenario, the present disclosurecontemplates that a portion of a dataset becomes unavailable even tosystem-level entities due to, for example, hardware failure, systemfailure, cyberattack, data corruption, or some other event.

In some implementations, it is expected that data stored within thefirst storage system can survive certain data loss events, hardwarefailure events, or similar events. For example, the storage system maybe configured to execute data recovery operations, error correctionoperations, RAID rebuild operations, and so on. Such operations may notbe sufficient, however, to restore data in certain circumstances. Forexample, a system implementing a RAID-6 configuration may encounterfailures with more than a certain number of devices (e.g., more than twodevices), preventing data recovery operations such as RAID rebuildoperations from fully recovering any missing data. Similarly, a devicemay experience individual block read or data corruption errors duringreconstruction of a failed device as well. Additionally, some operationsmay require that the entirety of a dataset be retrieved from anothersource, such as a backup site. This may not be feasible in instanceswhere rapid data recovery is required, and may unnecessarily wasteprocessor, network, and storage resources to obtain the entirety of a(e.g., very large) dataset from a remote source even though it may beknown that only a small portion of the dataset has been corrupted.

The portion of the dataset that may become unavailable can include anysub-part of the entire dataset. For example, the portion of the datasetthat has become unavailable may be embodied as a single data block(e.g., a 1 MB block), as one or more pages within the data block, as oneor more bytes in the dataset, as one or more logical portions of thedataset (e.g., a file in a file system), as a segment (or a segment andoffset) in a metadata representation of the dataset, or in some otherway.

In the example method depicted in FIG. 4 , detecting 402 that a portionof the dataset stored in a first storage system has become unavailablemay be carried out automatically and without user intervention. Forexample, a controller within a particular storage system may beconfigured to detect a device failure, to detect a failed readoperation, to detect corrupted data during a scrubbing process, or insome other way. In other embodiments, the controller may receive amessage from the underlying storage media (e.g., a storage device)indicating that the portion of the dataset has become unavailable, orthat some event has occurred that would make the portion of the datasetunavailable.

The example method depicted in FIG. 4 also includes obtaining 404 anidentifier for the portion of the dataset that has become unavailable.The identifier for the portion of the dataset that has becomeunavailable may be embodied, for example, as a block identifier, as anidentifier associated with a metadata representation of the dataset(e.g., a segment and offset value), as an identifier of a snapshot ofthe dataset along with an offset identifying a location within thesnapshot that includes the portion of the dataset, or in some other way.The identifier for the portion of the dataset that has becomeunavailable may be obtained 404, for example, by identifying a mostrecent snapshot that includes the portion of the dataset and using anidentifier associated with the snapshot as the identifier (optionallyalong with an offset or other additional information that specifies theparticular portion of the snapshot that includes the portion of thedataset), by identifying a logical identifier associated with thedataset such as a file name, object name, or file name (optionally alongwith an offset or other additional information that specifies thelocation of the particular portion of the dataset), or some otheridentifier. Readers will appreciate that in different embodiments,different types of identifiers may be obtained 404 based on the natureof the data source that will ultimately be to find a replacement for theportion of the dataset. For example, in an embodiment where the portionof the dataset that is included in a first volume will be retrieved froma replica volume, a volume identifier may be obtained 404. In anembodiment where the portion of the dataset that is included in a firstvolume will be retrieved from a snapshot of the volume, however, asnapshot identifier may be obtained 404.

Readers will appreciate that in some embodiments, obtaining 404 anidentifier for the portion of the dataset that has become unavailablemay be carried out automatically and without user intervention. Forexample, applications executing on a controller (e.g., a storagecontroller application) may be configured with instructions that carryout the methods described herein, such that the applicationsautomatically detect that the portion of the dataset is unavailable andobtain 404 identifiers of the unavailable portion of the dataset withoutuser intervention. For example, such applications may be configured withinstructions that include data indicating reference tables or other datastores that store identifiers for portions of the dataset that arestored in a particular storage system. As another example, identifiersfor unavailable portions of the dataset may be provided to suchapplications during a replication process for portions of the dataset,during a process of updating/creating deduplication references, orduring any other process that involves the abovementioned portions ofthe dataset. Accordingly, when such applications detect that the portionof the dataset is unavailable, the applications can automatically,without user intervention, initiate a process of determining,identifying, or otherwise obtaining 404 one or more identifiers for theunavailable portion of the dataset using dataset identifier resourcesthat were previously provided or made accessible to the applications.

The example method depicted in FIG. 4 also includes locating 406, usingthe identifier, a replacement portion of the dataset that is stored atone or more other storage systems. In some implementations, the datasetis being (e.g., synchronously) replicated across a set of storagesystems. In other words, each storage system of the set may store areplica of the dataset such that portions of the dataset can beretrieved from any of the storage systems in the set. Accordingly, aportion of the dataset that is unavailable at one storage system may beavailable at one or more other storage systems of the abovementionedset. As described above, the unavailable data forms only a portion ofthe dataset, not the entire dataset. So the abovementioned identifierrefers specifically to a portion of the dataset, not the dataset as awhole (although an identifier of the entire dataset may be paired withadditional information to identify the portion of the dataset). Forexample, the identifier may be a block identifier for a dataset thatcomprises a large number of blocks that are distributed across aplurality of systems.

In certain cases, a replica of the dataset may be an asynchronousreplica of the dataset or a snapshot of the dataset. In such cases, itmay be necessary to ensure that the data contained in the asynchronousreplica or the snapshot corresponds to the data that became unavailable.In other words, the portion of the dataset that is obtained from anotherstorage system is a current version and not an older version of the datais being used to restore the portion of the dataset became unavailable.

The identifier of the portion of the dataset that is unavailable can beused to locate a replacement for the portion of the dataset thatcorresponds to the identifier. For example, as described above, theidentifier may be a logical address. In some implementations, a datablock may be referred to or stored with reference to a particularlogical address on each storage system of a set of storage systems thatare synchronously replicating the dataset. Accordingly, if the portionof the dataset is damaged on one storage system, its replacement may belocated with reference to its corresponding logical address on one ormore other storage systems. In some embodiments, if the same logicaladdress is used on two different storage systems to store replicas ofthe same portion of the dataset, both storage systems may be configuredto track overwrites on the portion of the dataset at the logicaladdress. This is to ensure that the portion of the dataset stored as areplica on a storage system is the same data as that which becameunavailable on another storage system.

In some implementations, locating 406 a replacement portion of thedataset can include identifying the other storage systems that may havereplacement portions of the dataset. For example, the storage system maymaintain replication information associated with the dataset, where suchreplication information identifies replication targets associated withthe dataset. Such information may be used to identify replication peers(i.e., other storage systems) for the dataset.

In some implementations, locating 406 a replacement portion of thedataset can include determining whether located data is actually areplica of the portion of the dataset that became unavailable. Forexample, a hash value of the located data may be compared to apreviously stored hash value associated with the portion of the datasetto determine whether the located data matches the portion of the datasetthat has become unavailable. In other embodiments, other values that arerepresentative of a dataset (or some portion thereof) such as a cyclicredundancy checksum (‘CRC’) value may be utilized. In still otherembodiments, time-based information can be used to make theabove-mentioned comparison. For example, if a storage system where aportion of the dataset becomes unavailable is aware that the unavailableportion was not written to or updated more recently than a replica(e.g., from another storage system), then the replica can be said to bethe same data as in the portion of the dataset that became unavailable.

If data at a second storage system is determined to be a replacement forthe portion of the dataset that became unavailable at a first storagesystem, the data located at the second storage system may be retrievedby the first storage system or otherwise be made available to the firststorage system. Readers will appreciate that only the portion of thedataset that is a replacement for the unavailable portion of the datasetmay be retrieved, without retrieving other portions of the dataset orthe entire dataset.

Readers will appreciate that locating 406 a replacement portion of thedataset may be carried out automatically and without user intervention.For example, a storage system controller or other entity may beconfigured with instructions that, when executed, carry out the stepsdescribed herein, such that the applications automatically locate 406the replacement portion of the dataset at one or more other storagesystems without user intervention.

The example method depicted in FIG. 4 also includes writing 408, to thedataset that is stored in the first storage system, the replacementportion of the dataset as a replacement of the portion of the datasetthat has become unavailable. Writing 408 the replacement portion of thedataset may occur automatically and without user intervention. Writing408 the replacement portion of the dataset as a replacement of theportion of the dataset that has become unavailable may be carried out,for example, by writing the replacement data to the first storage systemand updating a logical address (or even a metadata representation of thedataset) that previously referred to the unavailable portion of thedataset to now refer to location within the storage system where thereplacement portion was stored. As such, the replacement portion mayserve as a replacement of the portion of the dataset that has becomeunavailable, regardless of the fact that the replacement portion may bestored at a physical location within the first storage system thatdiffers from the physical location of the unavailable portion of thedataset. As a result, repairing the dataset may occur in a way that istransparent to applications that are accessing the dataset.

For further explanation, FIG. 5 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The method of FIG. 5 is similarto the method of FIG. 4 in that the example method depicted in FIG. 5also includes detecting 402 that a portion of the dataset stored in afirst storage system has become unavailable, obtaining 404 an identifierfor the portion of the dataset, locating 406, using the identifier, areplacement portion of the dataset that is stored at one or more otherstorage systems, and writing 408, to the dataset that is stored in thefirst storage system, the replacement portion of the dataset as areplacement of the portion of the dataset that has become unavailable,where the writing occurs automatically, without user intervention.

The example method of FIG. 5 differs from that of FIG. 4 in that theexample method depicted in FIG. 5 also includes obtaining 505replication metadata that was generated when replicating the datasetbetween multiple storage systems, wherein the identifier for the portionof the dataset is obtained based on the replication metadata. Thereplication metadata described herein may be any metadata that pertainsto the portion of the dataset that has become unavailable and that isgenerated or used during a process of replicating the portion of thedataset from the storage system where the portion of the dataset wasstored (e.g., a replication source) to or from one or more other storagesystems (e.g., a replication target).

In some implementations, replication metadata can include metadatarepresenting the portion of the dataset that became unavailable. Forexample, where the portion of the dataset is a data block, thereplication metadata can include a block identifier. The replicationmetadata can include a logical address of the portion of the datasetthat has become unavailable. The replication metadata can also (oralternatively) include other representations of the data block, such asa hash value. The replication metadata can also (or alternatively)include sequence information representing the portion of the dataset.For example, the portion of the dataset may be replicated along withmany other dataset portions in a particular sequence during areplication process. Accordingly, the replication metadata may include areplication sequence or order identifier identifying the position in thereplication sequence that corresponds to the portion of the dataset thathas become unavailable.

In some implementations, the replication metadata can include datarepresenting the replication target, such as one or more other storagesystems. The replication metadata may include an identifier for one ormore target storage systems that is a replication peer of the sourcestorage system that stored the portion of the dataset that becameunavailable. The replication metadata may include a storage volumeidentifier. The replication metadata can also (or alternatively) includea snapshot identifier where the portion of the dataset that becameunavailable was part of a snapshot. The replication metadata can also(or alternatively) include a logical address at the replication targetthat is to be used as a reference for the portion of the dataset thatbecame unavailable during replication or after the replication of theportion of the dataset is completed. The replication metadata can alsoinclude other metadata that is generated as a result of the replicationprocess. For example, the replication process may be configured togenerate metadata that represents replication checkpoints or milestones.More specifically, the replication process may have included thereplication of a number of portions of the dataset (e.g., in response toa particular trigger such as another application confirming an update tothe dataset) and generation of checkpoint data indicating a percentagevalue or count that indicates how many of the specified portions of thedataset have been replicated, how many have not yet been replicated, andso forth.

For further explanation, FIG. 6 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The example method of FIG. 6 issimilar to the method of FIG. 4 in that the method depicted in FIG. 6also includes detecting 402 that a portion of the dataset stored in afirst storage system has become unavailable, obtaining 404 an identifierfor the portion of the dataset, locating 406, using the identifier, areplacement portion of the dataset that is stored at one or more otherstorage systems, and writing 408, to the dataset that is stored in thefirst storage system, the replacement portion of the dataset as areplacement of the portion of the dataset that has become unavailable,where the writing occurs automatically, without user intervention.

In some implementations, the example method of FIG. 6 differs from thatof FIG. 4 in that the example method depicted in FIG. 6 also includesobtaining 605 a deduplication hash value for the portion of the dataset.In some implementations, deduplication processes are executed to ensurethat data that is being written to (or already stored by) one or morestorage systems is not a duplicate of other data that currently existson the one or more storage systems. In some implementations, thesededuplication processes generate representative values of the data, suchas a deduplication hash value. For example, a deduplication hash valuefor some block of data may be generated by using the block of data asinput to a hash function. In such an example, the deduplication hashvalue for the block of data may be compared to hash values that weregenerated by using blocks of data that are already stored by the storagesystem as inputs to the same hash function. These deduplication hashvalues associated with data that is already stored by the storage systemcan be stored as metadata that is associated with the data, or stored insome other way.

In some implementations, the deduplication hash value may have beenpreviously created for a portion of the dataset that later becomesunavailable. For example, a storage system may experience a failure thatcauses a portion of the dataset to become unavailable. However, metadataassociated with that portion of the dataset, such as the deduplicationhash value, may have been unaffected by the failure and still beavailable. Moreover, the portion of the dataset may have been replicatedto or be otherwise available at one or more other storage systems. Theone or more other storage systems may have an available replacement ofthe portion of the dataset that became unavailable, such that there is astored deduplication hash value for the replacement portion at the oneor more other storage systems (or the deduplication hash value can begenerated for the replacement portion and compared to the deduplicationhash value for the portion of the dataset that became unavailable).Accordingly, the deduplication hash value for the portion of the datasetthat has become unavailable can be used to identify and obtain areplacement for the unavailable portion of the dataset.

For further explanation, FIG. 7 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The example method of FIG. 7 issimilar to the method of FIG. 4 in that the example method depicted inFIG. 7 also includes detecting 402 that a portion of the dataset storedin a first storage system has become unavailable, obtaining 404 anidentifier for the portion of the dataset, locating 406, using theidentifier, a replacement portion of the dataset that is stored at oneor more other storage systems, and writing 408, to the dataset that isstored in the first storage system, the replacement portion of thedataset as a replacement of the portion of the dataset that has becomeunavailable, where the writing occurs automatically, without userintervention.

In some implementations, the example method of FIG. 7 differs from thatof FIG. 4 in that the example method depicted in FIG. 7 also includesobtaining 705 a file system identifier associated with the portion ofthe dataset that has become unavailable. The portion of the dataset thathas become unavailable may be stored in a file system that is supportedby an underlying storage system. Accordingly, there may be file systemmetadata associated with each portion of the dataset. For example, theportion of the dataset that has become unavailable may have been taggedwith information such as purpose, owner, environment, and so forth.Other file system metadata can include the file header, file folderdata, file path, file owner, author, date created, date modified, uniquefile identifiers, and so forth. The file system identifier or other filesystem metadata may include data structures or other resources thatstore associations between the abovementioned tags and portions of thedataset. Accordingly, the file system identifier for the portion of thedataset that has become unavailable can be used to identify and obtain areplacement for the unavailable portion of the dataset.

For further explanation, FIG. 8 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The example method of FIG. 8 issimilar to the method of FIG. 4 in that the example method depicted inFIG. 8 also includes detecting 402 that a portion of the dataset storedin a first storage system has become unavailable, obtaining 404 anidentifier for the portion of the dataset, locating 406, using theidentifier, a replacement portion of the dataset that is stored at oneor more other storage systems, and writing 408, to the dataset that isstored in the first storage system, the replacement portion of thedataset as a replacement of the portion of the dataset that has becomeunavailable, where the writing occurs automatically, without userintervention.

In some implementations, the method of FIG. 8 differs from that of FIG.4 in that the example method depicted in FIG. 8 also includes obtaining805 a cblock identifier associated with the portion of the dataset thathas become unavailable. As used herein, the term “cblock” may refer to acompressed block of data that is created using a data compressionalgorithm. In some implementations, the dataset may be stored as one ormore compressed blocks of data such that the portion of the dataset thathas become unavailable may be associated with a cblock identifier.Accordingly, the cblock identifier for the portion of the dataset thathas become unavailable can be used to identify and obtain a replacementfor the unavailable portion of the dataset.

For further explanation, FIG. 9 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The method of FIG. 9 is similarto the method of FIG. 4 in that the example method depicted in FIG. 9also includes detecting 402 that a portion of the dataset stored in afirst storage system has become unavailable, obtaining 404 an identifierfor the portion of the dataset, locating 406, using the identifier, areplacement portion of the dataset that is stored at one or more otherstorage systems, and writing 408, to the dataset that is stored in thefirst storage system, the replacement portion of the dataset as areplacement of the portion of the dataset that has become unavailable,where the writing occurs automatically, without user intervention.

In some implementations, the method of FIG. 9 differs from that of FIG.4 in that the method also includes obtaining 905 a private identifierthat is not visible to applications that utilize the dataset for theportion of the dataset that has become unavailable. The dataset may bestored in different formats and with reference to different logicalorganizations, some of which may not be visible to a user of thedataset. For example, users of the dataset may see a volume-basedinterface for accessing the dataset, where blocks of data for thedataset are logically addressed in terms of a volume identifier and acorresponding offset identifier. However, the dataset may be maintainedusing a different logical organization internally that is not visible tooutside users. For example, the dataset may be logically organized usingan internal metadata representation of the dataset, such that each datablock is accessed using internal metadata constructs. Accordingly, theprivate identifier for the portion of the dataset that has becomeunavailable can be used to identify and obtain a replacement for theunavailable portion of the dataset.

For further explanation, FIG. 10 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The example method of FIG. 10 issimilar to the example method of FIG. 4 in that the method depicted inFIG. 10 also includes detecting 402 that a portion of the dataset storedin a first storage system has become unavailable, obtaining 404 anidentifier for the portion of the dataset, locating 406, using theidentifier, a replacement portion of the dataset that is stored at oneor more other storage systems, and writing 408, to the dataset that isstored in the first storage system, the replacement portion of thedataset as a replacement of the portion of the dataset that has becomeunavailable, where the writing occurs automatically, without userintervention.

The example method of FIG. 10 differs from that of FIG. 4 in that theexample method depicted in FIG. 10 also includes obtaining 1005 theidentifier from an application that was utilizing the dataset prior tothe portion of the dataset becoming unavailable. An application may beprocessing portions of the dataset when a failure occurs, causing aparticular portion of the dataset to become unavailable. Readers willappreciate that the application may generate metadata, logs, or otherapplication data that can be used to identify and locate the portion ofthe dataset that has become unavailable. For example, the applicationmay generate application execution logs that indicate the portions ofthe dataset that the application was processing. An execution log mayindicate, for example, sequence numbers for blocks of data that werebeing processed. The sequence numbers may indicate that, for example,block 30 of 50 blocks of the dataset had completed processing and block31 was being processed before the failure occurred. Accordingly, theidentifier for the portion of the dataset that became unavailable (e.g.,‘block 31’) may be obtained based on an application's processing of thedataset. As another example, metadata or logs generated by theapplication that was utilizing the dataset may indicate that the portionof the dataset that has become unavailable was replicated to anotherstorage system within a threshold period of time prior to the failure.Such information can be used to identify the portion of the dataset thathas become unavailable and locate the replica based on replicationmetadata provided by the application.

For further explanation, FIG. 11 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The example method of FIG. 11 issimilar to the method of FIG. 4 in that the example method depicted inFIG. 11 also includes detecting 402 that a portion of the dataset storedin a first storage system has become unavailable, obtaining 404 anidentifier for the portion of the dataset, locating 406, using theidentifier, a replacement portion of the dataset that is stored at oneor more other storage systems, and writing 408, to the dataset that isstored in the first storage system, the replacement portion of thedataset as a replacement of the portion of the dataset that has becomeunavailable, where the writing occurs automatically, without userintervention.

In some implementations, the method of FIG. 11 differs from that of FIG.4 in that the example method depicted in FIG. 11 also includes querying1102, using the location, the one or more other storage systems forportions of the dataset that are located at a corresponding locationwithin the one or more other storage systems, wherein the locationincludes one or more of a segment identifier and a segment offset value.In addition to the methods described above, the location of a portion ofthe dataset that has become unavailable may be expressed as a segmentidentifier and offset, which can be used to query other storage systemsfor replacement portions of the dataset. In some embodiments, a storagedevice may be logically divided into multiple segments, each identifiedby a segment identifier. In other embodiments, the logical location of aportion of data may be referred to using a segment identifier and anoffset value that is with reference to another value, such as thesegment identifier. In certain cases, each of a number of storagesystems may have replica portions of the dataset being stored at thestorage system at the same segment identifier and segment offset valueat approximately the same time during a replication process. In suchcases, a combination of segment identifier and segment offset value plusa time value can represent a globally unique identifier for a portion ofa dataset. For example, in cases where the dataset is being replicated(e.g., synchronously or asynchronously) to different storage systems,portions of the dataset may be referenced using the same logical addressidentifiers across each of the different storage systems. In otherwords, a portion of the dataset that is stored with reference to aparticular segment ID and offset on one storage system may be referencedusing that same segment ID and offset on one or more other storagesystems. Given this, the segment ID and offset can be used to queryother storage systems for a replacement of the portion of the datasetthat has become unavailable.

In some implementations, the method also includes determining 1104 thatthe replacement portion of the dataset is available at the correspondinglocation at the one or more other storage systems. For example, theabovementioned querying process may return a number of results. Forexample, some storage systems may return a result of the queryindicating that nothing is currently stored with reference to thelogical segment ID and offset. Other storage systems may return a numberof dataset portions. In some implementations, any of the returneddataset portions may be a replacement for the portion of the datasetthat has become unavailable just because these returned dataset portionswere referenced by the segment ID and offset that was used for theportion of the dataset that became unavailable. In otherimplementations, the returned dataset portions may be validated usingmethods such as comparison of hash values and so on.

For further explanation, FIG. 12 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The example method of FIG. 12 issimilar to the method of FIG. 4 in that the example method depicted inFIG. 12 also includes detecting 402 that a portion of the dataset storedin a first storage system has become unavailable, obtaining 404 anidentifier for the portion of the dataset, locating 406, using theidentifier, a replacement portion of the dataset that is stored at oneor more other storage systems, and writing 408, to the dataset that isstored in the first storage system, the replacement portion of thedataset as a replacement of the portion of the dataset that has becomeunavailable, where the writing occurs automatically, without userintervention.

In some implementations, the method of FIG. 12 differs from that of FIG.4 in that the example method depicted in FIG. 12 also includesidentifying 1202 data whose similarity to the portion of the dataset(that has become unavailable) satisfies a threshold similarity. Incertain circumstances, a dataset portion that is similar but notidentical to the portion of the dataset that has become unavailable maybe acceptable. For example, metadata for the portion of the dataset thathas become unavailable may define rules or conditions for acceptablereplacements of the portion of the dataset. As a more specific example,the metadata for the portion of the dataset that has become unavailablemay indicate that, if the portion of the dataset is corrupted or lost, areplacement data portion having a 95% or 99% similarity to the portionof the dataset that has become unavailable may be an acceptablereplacement.

In other implementations, a replacement data portion may be convertibleinto the portion of the dataset that has become unavailable by executionof one or more operations on the replacement data portion. For example,an application may be processing the dataset such that each sequentialportion of a dataset is greater than the previous portion of the datasetin the sequence by some fixed value. Accordingly, a replacement portionmay be located that is, for example, the immediately preceding portionof the dataset in the particular sequence relative to the portion of thedataset that became unavailable. Such a near-replacement data portionmay be incrementable by the fixed value described above, in order toobtain a replacement of the portion of the dataset that becameunavailable.

For further explanation, FIG. 13 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The example method of FIG. 13 issimilar to the method of FIG. 4 in that the example method depicted inFIG. 13 also includes detecting 402 that a portion of the dataset storedin a first storage system has become unavailable, obtaining 404 anidentifier for the portion of the dataset, locating 406, using theidentifier, a replacement portion of the dataset that is stored at oneor more other storage systems, and writing 408, to the dataset that isstored in the first storage system, the replacement portion of thedataset as a replacement of the portion of the dataset that has becomeunavailable, where the writing occurs automatically, without userintervention.

In some implementations, the method of FIG. 13 differs from that of FIG.4 in that the example method depicted in FIG. 13 also includesdetermining 1302 that a first timestamp associated with storage of thereplacement portion of the dataset is not earlier than a secondtimestamp associated with storage of the portion of the dataset thatbecame unavailable. In some implementations, if the replacement dataportion has a storage timestamp that is within a threshold period oftime after the storage timestamp of the portion of the dataset that hasbecome unavailable, the replacement data portion may be acceptable as areplacement for the portion of the dataset that has become unavailable.

For further explanation, FIG. 14 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The example method of FIG. 14 issimilar to the method of FIG. 4 in that the example method depicted inFIG. 14 also includes detecting 402 that a portion of the dataset storedin a first storage system has become unavailable, obtaining 404 anidentifier for the portion of the dataset, locating 406, using theidentifier, a replacement portion of the dataset that is stored at oneor more other storage systems, and writing 408, to the dataset that isstored in the first storage system, the replacement portion of thedataset as a replacement of the portion of the dataset that has becomeunavailable, where the writing occurs automatically, without userintervention.

In some implementations, the method of FIG. 14 differs from that of FIG.4 in that the example method depicted in FIG. 14 also includesdetermining 1402 that a first checksum value associated with thereplacement portion of the dataset equals a second checksum valueassociated with the portion of the dataset. For example, a checksum(e.g., a cyclic redundancy checksum (CRC)) value for the replacementdata portion may be compared to a stored checksum value for the portionof the dataset that has become unavailable. If the values match, thereplacement data portion may be stored as a replacement for the portionof the dataset that has become unavailable. In some embodiments, the CRCvalue may be a cryptographic checksum value.

For further explanation, FIG. 15 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The method of FIG. 15 is similarto the method of FIG. 4 in that the method depicted in FIG. 15 alsoincludes detecting 402 that a portion of the dataset stored in a firststorage system has become unavailable, obtaining 404 an identifier forthe portion of the dataset, locating 406, using the identifier, areplacement portion of the dataset that is stored at one or more otherstorage systems, and writing 408, to the dataset that is stored in thefirst storage system, the replacement portion of the dataset as areplacement of the portion of the dataset that has become unavailable,where the writing occurs automatically, without user intervention.

In some implementations, the method of FIG. 15 differs from that of FIG.4 in that the example method depicted in FIG. 15 also includesdetermining 1508 that a first error correction code value associatedwith the replacement portion of the dataset equals a second errorcorrection code value associated with the portion of the dataset.Similar to the method described above with respect to FIG. 14 , forexample, an error correction code for the replacement data portion maybe compared to a stored error correction code for the portion of thedataset that has become unavailable. If the values match, thereplacement data portion may be stored as a replacement for the portionof the dataset that has become unavailable.

For further explanation, FIG. 16 sets forth a flow chart illustrating anexample method of restoring lost data in accordance with someembodiments of the present disclosure. The example method of FIG. 16 issimilar to the method of FIG. 4 in that the example method depicted inFIG. 16 also includes detecting 402 that a portion of the dataset storedin a first storage system has become unavailable, obtaining 404 anidentifier for the portion of the dataset, locating 406, using theidentifier, a replacement portion of the dataset that is stored at oneor more other storage systems, and writing 408, to the dataset that isstored in the first storage system, the replacement portion of thedataset as a replacement of the portion of the dataset that has becomeunavailable, where the writing occurs automatically, without userintervention.

In some implementations, the example method of FIG. 16 differs from thatof FIG. 4 in that the example method depicted in FIG. 16 also includesdetermining 1610 a location at the first storage system that wasassociated with the portion of the dataset. As described above, theportion of the dataset that has become unavailable may be referred tousing a logical address (e.g., <volume, offset> or <segment, offset> andso forth) or a physical address (physical memory address, sectorlocation, and so forth). In some implementations, the logical addressmay be used to identify the portion of the dataset that has becomeunavailable and to locate its replacement at another storage system.Such information may be used to identify the portion of the dataset thathas become unavailable and locate its replacement at another storagesystem.

In some implementations, the method also includes writing 1612 thereplacement dataset portion to the determined location, such as byupdating the logical address referencing the portion of the dataset thatbecame unavailable to now reference the replacement dataset portion. Inother words, the portion of the dataset that became unavailable losesthe logical address reference that could previously be used to accessthe portion of the dataset that has become unavailable. After thereference is updated, the logical address can be used to refer to thereplacement portion of the dataset. In other implementations, thereplacement dataset portion may be written to a different logicaladdress rather than the old logical address for the portion of thedataset that became unavailable. In such a case, additional updates maybe made to ensure consistency, such as updating a mapping of logicaladdresses to physical addresses by mapping the different logical addressto a same physical address as the old logical address.

For further explanation, FIG. 17 sets forth a flow chart illustrating anexample method of recovering corrupted data through speculative bitflipand cross-validation. The example method of FIG. 17 may be implementedby one or more storage systems such as the storage systems describedabove with reference to FIGS. 1A-3E. In some embodiments, the stepsdescribed herein may be performed by one or more storage controllers(e.g., storage controllers 110A-D, shown in FIG. 1A, or storagecontroller 101, shown in FIG. 1B) that are included within a particularstorage system. In other embodiments, the steps may be performed (atleast to some extent) by fleet management software or some othersoftware that is responsible for the management of a fleet of storagesystems (e.g., two or more storage systems).

As discussed above, data stored in storage devices (e.g., storage drives171A-F, shown in FIG. 1A) can be stored in one or more portions. As usedherein, the term ‘data portion’ may refer to a data block, object, file,page, shard, stripe, or the like. In an example embodiment, data may bestored as Flash pages, which may be a few kilobytes in size. A Flashblock may include a large number of Flash pages. A Flash page may befurther subdivided into a header portion and content portion. There maybe more than one header for a Flash page. In addition, there may beanother header within a Flash block that stores information associatedwith each Flash page.

In some implementations, a storage device may implement error detectionand/or error correction methods to detect or correct errors in storeddata. These methods may run automatically or on request by anadministrator. For example, a storage device (such as a NAND flash chip)may include redundancy schemes such as error correction codes that maytolerate corrupted bits within a storage device, or erasure codes thattolerate the failure of multiple storage nodes, and so on. Additionally,a low density parity check (‘LDPC’) code may be used, or a checkbit,parity bit, checksum, and/or other error detection or error correctionmethods.

However, readers will appreciate that the error detection or correctionmethods may fail to identify all errors that may exist in data. Forexample, a data loss event (e.g., a cyberattack or other event requiringdisaster recovery) may occur that results in some data blocks beingirretrievably lost. In other circumstances, certain data blocks can beretrieved but their contents cannot be verified as being accurate to atime prior to the data loss event. Independent of data loss events orlarge-scale data corruption events, small errors may be introduced intothe data over time. A storage device may provide these data blocks andeven perform error detection and correction but be unable to verifywhether all errors that were introduced have been identified andcorrected.

Moreover, existing error detection methods may be able to identify thepresence of an error, but not its precise location. Similarly, a dataloss event may result in the loss of blocks of data whose retrievalrelies on the accuracy of other data blocks. For example, in certainRAID configurations, lost data blocks can be retrieved by performingparity calculations that use other data blocks, which must themselvesinclude accurate data if the lost data blocks are to be correctlyreconstructed. If the other data blocks include errors, they may not beusable to correctly reconstruct the lost data blocks. In other words,even where the data is retrievable from a storage device, errorcorrection methods may indicate corruption in enough data blocks suchthat an entire section of data (such as a RAID stripe) is unusable. Inother circumstances, a storage system administrator or customer may wishto detect the presence of errors in data before any errors affect systemperformance. For example, during a storage system scrubbing process, anadministrator may wish to check for the presence of errors in the data.

One example of an error may be a bit flip error, where one or more bitsin a data block change in value from 0 to 1 or from 1 to 0. Bit fliperrors can occur unexpectedly and the location of the bit flip within adata block, such as in a Flash page, may not be known. Bit flip errorsmay occur during a data loss event, or even due to factors such asrepeated use of the storage medium (e.g., a large number of P/E cyclesoccurring in Flash memory), environmental factors such as heat orradiation, or inaccuracies introduced during data storage or copying,among other reasons. Multiple bit flip errors may occur within a singledata block, and can occur in a header portion or content portion. Bitflip errors can even occur within an additional portion that is appendedto or otherwise added to the data block for purposes of error detectionor error correction, such as within a parity bit portion or cyclicredundancy check (CRC) block that is appended to a data portion. Sincethe bit position of a bit flip error may not be obviously identifiable,the corruption in the data may become evident only when a consumer's(e.g., an application's) use of the data produces erroneous results.

The disclosed systems and methods are directed to correcting such errorsby altering the data portion in a speculative manner in order to reversethe effects of the data corruption. Accordingly, the example methoddepicted in FIG. 17 includes altering 1702 a first data portion storedin a storage device, wherein the first data portion contains an error.Altering 1702 the first data portion stored in the storage device,wherein the first data portion contains an error can be performed in avariety of ways. As described above, the storage device (e.g., storagedrives 171A-F, shown in FIG. 1A) may store data in data portions. Eachdata portion may be of a fixed size, such as 512 bytes or 4 kilobytes.As described above, errors may be introduced into the data portion overtime such that an error is detected but the nature of the error or theprecise location of the error inside the data portion is unknown.Accordingly, the method includes altering the first data portion in anattempt to undo the error, given that, for example, a data loss eventcan cause corruptions within data. Since the location of the errorwithin the data portion is initially not known, altering the first dataportion to undo the error can include, for example, changing asub-portion (e.g., a first sub-portion) of the data portion in aspeculative manner. For example, where the data portion comprises a setof bytes, altering the first data portion can include altering the valueof one or more bits of the first data portion. For example, the storagecontroller's described herein may copy the data portion into a registeror to another location in memory and then attempt to modify asub-portion of the data portion. Such a modification may be part of ascheme that speculates that the error is within that sub-portion andthat modifying that sub-portion will resolve the error.

For any data portion, such as a Flash page of size 4 KB, a hash functioncan be used to generate a hash value that represents the data containedin the 4 KB page. Readers of skill in the art will understand that anysuitable hash function can be used to generate the hash value, where ahash function is designed to generate a fixed-size output value from aninput while minimizing collisions, or the probability that the sameoutput value is generated from two different inputs. Hash values for adata portion from different points in time can then be compared to eachother to determine whether the data portion's contents have changedbased on whether the hash values from different points in time matcheach other or not.

An example of a hash function may be the use of a cyclic redundancycheck (or checksum) (CRC) method. Readers will appreciate that the firstdata portion, when initially stored, may have been stored with a CRCvalue that is generated from the first data portion and then appended tothe first data portion. CRC values may be generated, for example, byperforming a mathematical calculation using the data of the first dataportion. As a more particular example, the mathematical calculations caninclude polynomial long division, in which a chosen generator polynomialbecomes the divisor, the data of the first data portion becomes thedividend, and the remainder is the result, or the obtained CRC value.The obtained CRC value may itself be of a certain number of bits, or nbits. As an even more specific example, a 1-bit CRC, also referred to asa parity bit, may be a single CRC bit added to the first data portion,where the single bit indicates a count of 1-bits (or 0-bits) within thefirst data portion. In other words, the CRC value can be said to berepresentative of the data of the first data portion.

Readers will appreciate that corruption within the first data portion(e.g., a bitflip error) can cause the appended CRC value to no longer berepresentative of the first data portion. For example, prior to any datacorruption, the first data portion may be 7 bits of data that are allzeroes, i.e., ‘0000000’. For this data portion, an appended CRC value,such as a 1-bit CRC or parity bit, represents a count of 1-bits in thefirst data portion. There are no 1-bits in this data portion so, in aneven parity bit scheme, the parity bit may also be set to 0. This isbecause the parity bit is set to 0 in an even parity bit scheme if thecount of 1-bits is an even number (in this case, also zero) or 1 if thecount is an odd number. More generally, an ‘even parity’ scheme is aspecial case of a CRC, where the 1-bit CRC is generated by thepolynomial x+1. The parity bit is also counted toward the count of1-bits in the parity bit scheme. The final result with the CRC valuebeing appended to the first data portion stored will be ‘00000000’. Asanother example, the CRC value itself may be 4 bits (e.g., 1010) or abyte (e.g., 10101101) that is generated using a different method (e.g.,using a specific generator polynomial) that is different from the paritybit example outlined above.

Subsequently, data corruption in the first data portion may result inchanged values for one or more bits of the first data portion. As aresult, the CRC value may no longer be representative of the data of thefirst data portion. As an example, the CRC value of 1010 may have beeninitially calculated using the data of the first data portion and thenappended to the first data portion. Subsequent to data corruptionoccurring within the first data portion, the data in the first dataportion will have changed and the CRC value of 1010 may or may not berepresentative of the data of the first data portion. As a result, if aCRC value of the first data portion is calculated after data corruptionis known to have occurred within the first data portion, the newlycalculated CRC value for the corrupted data portion may be different(i.e., not 1010 but 1110 or some other value). If the CRC value appendedto the first data portion is different from the CRC value calculated forthe first data portion, it may be concluded that data corruption existswithin the first data portion.

The systems and methods described herein involve a speculative dataalteration scheme where it is speculated that if a certain sub-portionof the data is altered, the errors introduced due to data corruption maybe resolved and the corruption may be reversed. Accordingly, the firstdata portion is altered, as described above in the step of altering1702, in an attempt to resolve the error. More specifically, it isspeculated that if a specific alteration is made, then a CRC value thatis calculated using the altered first data portion should match thestored CRC value that was initially appended to the first data portion.Accordingly, the example method depicted in FIG. 17 also includescalculating 1704 a hash value 1705 using the altered first data portion.Calculating 1704 the hash value 1705 using the altered first dataportion can be carried out in a variety of ways. As discussed above, aCRC value may be calculated using data of the altered first dataportion.

The example method depicted in FIG. 17 also includes comparing 1706 thehash value 1705 calculated using the altered first data portion to astored hash value for the first data portion. As discussed above, theCRC value may be said to represent the data of the first data portionsuch that the CRC value (or any hash value in general) may be used todetect the presence of errors within the first data portion. Supposedthat the stored CRC value for the first data portion is termed CRC-A,the value being 1010. Then a portion is altered (e.g., a bit is flipped)within the first data portion and a CRC value, termed CRC-B, iscalculated using data of the altered first data portion.

The example method depicted in FIG. 17 also includes determining 1708,based on the comparing, whether the altering corrects the error in thefirst data portion. Continuing with the example above, if CRC-B=CRC-A(i.e., CRC-B is also calculated to be 1010), then it may be determinedthat the specific alteration made to the first data portion (i.e., thespecific bit that was flipped) resolved the error. The altered firstdata portion can be considered to be a restored version of the firstdata portion, prior to data corruption having been introduced into thefirst data portion.

Conversely, if CRC-B≠CRC-A (i.e., CRC-B is calculated to be not 1010 butsome other value like 1000), then it may be determined that the specificalteration made to the first data portion (i.e., the specific bit thatwas flipped) did not resolve the error. If so, the method may return tothe step of altering 1702 and alter a different sub-portion (e.g., adifferent bit) of the first data portion, then re-calculate the CRC (orother hash value) for the re-altered first data portion, and determinewhether the re-calculated CRC value matches the stored CRC value in thefirst data portion.

For further explanation, FIG. 18 sets forth a flow chart illustrating anexample method of recovering corrupted data through speculative bitflipand cross-validation. The example method of FIG. 18 is similar to theexample method of FIG. 17 in that the example method of FIG. 18 alsoincludes altering 1702 a first data portion stored in a storage device,wherein the first data portion contains an error, calculating 1704 ahash value 1705 using the altered first data portion, comparing 1706 thehash value 1705 calculated using the altered first data portion to astored hash value for the first data portion, and based on thecomparing, determining 1708 whether the altering corrects the error inthe first data portion.

The example method of FIG. 18 differs from the example method of FIG. 17in that in the example method of FIG. 18 , altering the first dataportion also includes flipping 1802 at least one bit in the first dataportion. As described above, altering the first data portion can includemaking changes to a sub-portion of the first data portion, such as asingle bit. Altering the single bit can include changing its value from0 to 1 or from 1 to 0. For example, a storage controller can copy outdata from a storage device that corresponds to the first data portioninto another storage area or memory area and alter the data for thefirst data portion.

As discussed above, this process involves speculation as to the bit thatinitially got flipped due to corruption such that if the bit were‘flipped back’, the corruption would be resolved. Since the precise bitposition may not be known, the speculative bit flipping may proceed byflipping, for example, the very first bit in the first data portion. Forexample, for the bits in an 8-bit byte having index values 0-7, flippingthe bit can include flipping the bit at position 0, or the leftmost bitin the byte, or flipping the bit can include flipping the bit atposition 7, or the rightmost bit in the byte, and so on.

Bit flipping as described above can be performed in a variety of ways.Various bitwise manipulation functions or operators can be used. As anexample, the first data portion can be processed with an XOR functionthat includes a mask. As a more specific example, suppose that thebinary representation of the first data portion (or the first four bitsof the first data portion) is 1101. Then flipping the first bit of thisfirst data portion can include XOR-ing 1101 with a mask of 1000 whereby1101 XOR 1000=0101. In other words, the first bit of this first dataportion has been deliberately altered, or flipped from 1 to 0. So 1101is now 0101. As mentioned above, this is done as part of a speculativebit flipping that speculates that earlier in time (e.g., due to datacorruption), that first bit was erroneously flipped from 0 to 1.

Altering the first data portion can also include flipping more than onebit, such as flipping two bits or more bits at a time in the first dataportion. Similar to the example described above, flipping two or morebits can also involve various bitwise manipulation functions such as XORor other logic functions and additional calculation components such asmasks. Readers will appreciate that bit flipping may be performed inparallel such that multiple bits are flipped during a speculativeprocess of identifying the bit that was corrupted (i.e., whose value wasinitially flipped due to data corruption, leading to an error).

The example method of FIG. 18 further differs from the example method ofFIG. 17 in that in the example method of FIG. 18 , flipping 1802 the atleast one bit also includes flipping 1804 a bit within a header portionof the first data portion.

Readers will further appreciate that where the first data portion is,for example, a Flash memory page, the Flash page may include a headerportion and a content portion. The header portion may include detailsregarding the Flash page, such as whether the Flash page is a data pageor a map page. The header portion may include the CRC value thatcorresponds to that Flash page. Accordingly, flipping 1802 the at leastone bit can include flipping a bit in the header portion of the Flashpage. Flipping 1802 the at least one bit can also include flipping a bitin the CRC value stored in the header portion as well. Moreover, theremay be multiple CRC values stored in the header or elsewhere in thefirst data portion. Flipping 1802 the at least one bit can includeflipping bits at any location within the first data portion. Asdescribed above, once any bit or set of bits is flipped, the CRC valuefor the altered first data portion is calculated and compared to the CRCvalue stored within the first data portion.

Readers will appreciate that the speculative bit flipping process can beoptimized or improved over time based on prior experience. For example,it may be identified that, for a particular storage device, a bit in aspecific position is always or nearly always the source of a bit fliperror. If so, that same bit may be flipped first during a subsequentdata loss or data corruption event, based on prior knowledge that thisbit is commonly the source of bit flip errors. In other words, the bitflipping process may not commence at the first bit or some presetposition each time but may be updated to initiate at the specific bitposition that is observed to be the source of most or all errors in sucha storage device. Moreover, a bit or set of bits at the same position(s)may be targeted in other storage devices that are of the same type,model, or configuration, or even storage devices that are of differenttypes.

For further explanation, FIG. 19 sets forth a flow chart illustrating anexample method of recovering corrupted data through speculative bitflipand cross-validation. The example method of FIG. 19 is similar to theexample method of FIG. 17 in that the example method of FIG. 19 alsoincludes altering 1702 a first data portion stored in a storage device,wherein the first data portion contains an error, calculating 1704 ahash value 1705 using the altered first data portion, comparing 1706 thehash value 1705 calculated using the altered first data portion to astored hash value for the first data portion, and based on thecomparing, determining 1708 whether the altering corrects the error inthe first data portion.

The example method of FIG. 19 differs from the example method of FIG. 17in that in the example method of FIG. 19 , calculating the hash valueusing the altered first data portion includes calculating 1902 a cyclicredundancy check (CRC) value using the altered first data portion. Asdescribed above, CRC may be a method for detecting errors in storeddata, whereby a CRC value is generated from data, such as a set ofbytes, and stored with the data at a first point in time. The CRC valuecan be recalculated at a second point in time with the same set of bytesand the recalculated CRC value is compared to the stored CRC value. Ifthere is a match, it provides a strong indication that no corruption wasintroduced into the data between the first point in time and the secondpoint in time. If the values differ, it may be determined that the datahas been corrupted (e.g., a bit flip error has occurred in the data).

Accordingly, the method of FIG. 19 includes calculating a CRC value forthe altered first data portion. More specifically, the first dataportion is altered, such as by flipping one of the bits of the firstdata portion. After the bit flip or data alteration, a new CRC value iscalculated for the altered first data portion. If this new CRC value forthe altered first data portion matches the stored CRC value (e.g., bothCRC values are 1110) that is obtained from the stored first dataportion, it may provide a strong indication (though not necessarily aguarantee, as discussed later) that there is no data corruption in thefirst data portion. Otherwise, if the new CRC value for the alteredfirst data portion does not match the stored CRC value that is obtainedfrom the stored first data portion (e.g., the stored CRC is 1110 but therecalculated CRC value is 1001), it may indicate the presence of datacorruption in the first data portion, such as one or more bit fliperrors.

For further explanation, FIG. 20 sets forth a flow chart illustrating anexample method of recovering corrupted data through speculative bitflipand cross-validation. The example method of FIG. 20 is similar to theexample method of FIG. 17 in that the example method of FIG. 20 alsoincludes altering 1702 a first data portion stored in a storage device,wherein the first data portion contains an error, calculating 1704 ahash value 1705 using the altered first data portion, comparing 1706 thehash value 1705 calculated using the altered first data portion to astored hash value for the first data portion, and based on thecomparing, determining 1708 whether the altering corrects the error inthe first data portion.

Readers of skill in the art will understand that the use of a hashfunction, such as a CRC, may not always provide detection of datacorruption such as bit flip errors with a high degree of probability. Aswith other hash functions, CRCs may suffer from aliasing or collisionproblems. In other words, two different inputs may yield the same CRCvalue. A comparison between CRC values of a stored first data portionand an altered first data portion can certainly provide a strongindication of whether corruption—such as bit flip errors—exists in thefirst data portion. But CRC comparison may not necessarily guaranteethat a data portion is free of bit flip errors. This is because thespeculative bit flip may, coincidentally, result in an altered firstdata portion whose CRC value is the same as the stored CRC for the dataportion. But the actual bit flip error may be in a different position.So the speculative bit flip at one position may coincidentally create analtered first data portion whose CRC value matches the stored CRC value,but the bit flip error may still exist at another position within thefirst data portion.

To cross-validate the CRC value generated using the altered first dataportion, further processing using RAID parity calculations may be used.Readers of skill in the art will understand that a RAID configurationmay be used to protect data in a storage device (e.g., storage drives171A-F). The RAID configuration may be applicable to the abovementionedfirst data portion, such that the first data portion may be considered aRAID shard that is a member of a collection of data portions or shardsthat is collectively termed a RAID stripe. In RAID configurations, datathat is stored in a storage device is protected through the calculationand storage of parity data that is generated using the data. Where thefirst data portion is a shard of a RAID stripe, the first data portioncan be used as part of RAID parity calculations to generate the paritydata, stored as parity data shards. Taken together, data shards andparity shards can be used to reconstruct data portions that are lost,corrupted, or otherwise unavailable. RAID parity calculations can thusbe used to cross-validate the altered first data portion. Morespecifically, the altered first data portion can be used in RAID paritycalculations to reconstruct another data portion (e.g., another dataportion that is part of the same RAID stripe as the first data portion).The contents of the reconstructed data portion can be further validatedthrough CRC comparison, memory comparison, or other methods. If suchvalidation succeeds for the reconstructed data portion, this may providea stronger indication that the alteration in the first data portionserved to resolve the bit flip error. This is because RAID paritycalculations using the altered first data portion would not result in avalidated reconstructed data portion if the alteration in the first dataportion did not resolve errors within the first data portion.

The example method of FIG. 20 differs from the example method of FIG. 17in that in the example method of FIG. 20 also includes reconstructing2002 a second data portion of a RAID stripe with parity calculationsthat use the altered first data portion. Reconstructing 2002 a seconddata portion of a RAID stripe with parity calculations that use thealtered first data portion may be performed in a variety of ways. Forexample, FIG. 20 shows a RAID stripe 2050 that includes shard A1 2010,shard A2 2012, shard A3 2014, shard A_(P) 2016, and shard A_(Q) 2018. Asshown in FIG. 20 , shard A1 2010, shard A2 2012, shard A3 2014, shardA_(P) 2016, and shard A_(Q) 2018 may be stored in storage devices 2001,2003, 2005, 2007, and 2009, respectively. In the example shown in FIG.20 , shard A1 2010, shard A2 2012, shard A3 2014 may include data (suchas user data) that is stored in the storage devices by another device(e.g., an application host), whereas shard A_(P) 2016 and shard A_(Q)2018 may include parity data that is generated with RAID paritycalculations.

In this example, shard A1 2010 may represent the first data portion. Analtered version of the shard A1 2010 may be generated at step 1702,creating an altered shard A1′ 2011. The altered shard A1′ 2011 is thenused in RAID parity calculations to reconstruct another shard in theRAID stripe 2050. For example, the altered shard A1′ 2011, along withshard A3 2014, shard A_(P) 2016, and shard A_(Q) 2018, may be used toreconstruct shard A2 2012, resulting in a reconstructed shard A2′ 2013.

The example method of FIG. 20 also includes calculating 2004 a secondhash value using the reconstructed second data portion. Calculating 2004a second hash value using the reconstructed second data portion caninclude, for example, calculating a hash value using shard A2′ 2013. Forexample, a CRC value can be generated using the reconstructed shard A2′2013.

The example method of FIG. 20 also includes determining 2006 that thecalculated second hash value matches a stored hash value within thesecond data portion. Determining 2006 that the calculated second hashvalue matches a stored hash value within the second data portion caninclude determining that the hash value generated using shard A2′ 2013matches a stored hash value stored within shard A2 2012. Morespecifically, determining 2006 that the calculated second hash valuematches a stored hash value within the second data portion can includedetermining that the CRC value generated using shard A2′ 2013 matches astored CRC value stored within shard A2 2012.

The example method of FIG. 20 also includes determining 2008 that thealtering corrects the error in the first data portion, based on thedetermination 2006 that the calculated second hash value matches astored hash value within the second data portion. Particularly, if thereis a match between the CRC value generated using shard A2′ 2013 and thestored CRC value stored within shard A2 2012, this may indicate that thereconstructed shard A2′ 2013 and the original shard A2 2012 include thesame data. As a result, because reconstructed shard A2′ 2013 matchesoriginal shard A2 2012, then altered shard A1′ 2011 is also likely toinclude the same data that was originally stored in storage device 2010prior to a data loss or data corruption event. In other words, alteredshard A1′ 2011 may represent an uncorrupted shard A1 2010. Morespecifically, the RAID parity calculations that use altered shard A1′2011 to reconstruct shard A2′ 2013 resulted in a shard A2′ that was thesame as the original shard A2. Therefore, it may be concluded that theshard used to reconstruct shard A2 (i.e., shard A1′) also includes thedata that was originally stored within it prior to data corruption.Accordingly, in some implementations, it may be further concluded thatthe alteration of shard A1 2010 in step 1702 to generate altered shardA1′ 2011 served to resolve the bit flip error that originally may haveoccurred in shard A1 2010.

For further explanation, FIG. 21 sets forth a flow chart illustrating anexample method of recovering corrupted data through speculative bitflipand cross-validation. The example method of FIG. 21 is similar to theexample method of FIG. 20 in that the example method of FIG. 21 alsoincludes altering 1702 a first data portion stored in a storage device,wherein the first data portion contains an error, calculating 1704 ahash value 1705 using the altered first data portion, comparing 1706 thehash value 1705 calculated using the altered first data portion to astored hash value for the first data portion, based on the comparing,determining 1708 whether the altering corrects the error in the firstdata portion, reconstructing 2002 a second data portion of the RAIDstripe with parity calculations that use the altered first data portion;calculating 2004 a second hash value using the reconstructed second dataportion; determining 2006 that the calculated second hash value matchesa stored hash value within the second data portion; and based on thedetermination, determining 2008 that the altering corrects the error inthe first data portion.

The example method of FIG. 21 differs from the example method of FIG. 20in that in the example method of FIG. 21 also includes, determining 2102that the second data portion is unavailable. Continuing with the exampleabove in FIG. 20 , RAID stripe 2050 should include shard A1 2010, shardA2 2012, shard A3 2014, shard A_(P) 2016, and shard A_(Q) 2018. Butshard A2 2012 may be lost during a data loss event. For example, somecircumstance such as a severe power surge or natural disaster may causea partial or total loss of data stored in storage device 2003. As aresult, the data of shard A2 2012 may be partially or whollyunretrievable. As one example, the entirety of the shard A2 2012 may belost. As another example, the header portion of shard A2 2012 may beavailable but the remaining contents may be unavailable. In a relatedexample, while the some or all of the contents of shard A2 2012 may beunavailable, the storage device 2003 may still be able to providemetadata or other additional data associated with shard A2 2012. Thismetadata or other additional data may be accessible from a primaryheader, or primary page header, or another header, or another storagelocation in storage device 2003 or another storage device. Readers willappreciate that, as an example of this metadata, the CRC values or otherhash values that were generated and stored for the shard A2 2012 maystill be accessible.

The example method of FIG. 21 also includes, obtaining 2104 the storedhash value for the second data portion from the storage device based onthe determination 2102 that the second data portion is unavailable,wherein the storage device stores the stored hash value in a primaryheader associated with the second data portion. As discussed above,stored hash values (such as stored CRC values) for a data portion may beavailable at a storage device that stores the data portion, even ifother contents of the data portion are unavailable. Accordingly, in oneexample, where the second data portion is a Flash page, the stored hashvalue may be in a primary header, or primary page header for the Flashpage. Even if the Flash page itself is unavailable, a storage controllermay still be able to obtain, from the primary page header, a stored hashvalue or CRC value generated using data of the Flash page. Additionally,CRC values may be mirrored across multiple storage locations within thedata portion, within the storage device, or elsewhere in a storagesystem. In response to a determination that the second data portion isunavailable, the storage controller may be configured to search throughthese different locations to obtain the CRC value associated with theunavailable second data portion.

For further explanation, FIG. 22 sets forth a flow chart illustrating anexample method of recovering corrupted data through speculative bitflipand cross-validation. The example method of FIG. 22 is similar to theexample method of FIG. 17 in that the example method of FIG. 22 alsoincludes altering 1702 a first data portion stored in a storage device,wherein the first data portion contains an error, calculating 1704 ahash value 1705 using the altered first data portion, comparing 1706 thehash value 1705 calculated using the altered first data portion to astored hash value for the first data portion, and based on thecomparing, determining 1708 whether the altering corrects the error inthe first data portion.

The example method of FIG. 22 differs from the example method of FIG. 17in that in the example method of FIG. 22 also includes determining 2202that a first number of the plurality of data portions of the RAID stripedoes not contain an error. Continuing with the example above of FIG. 20, an initial determination may be made that altered shard A1′ 2011 andshard A_(P) 2016 do not contain errors. As a more specific example, thestorage controller may determine that shard A_(P) 2016 does not containerrors because no errors have been identified in usage of shard A_(P)2016. By contrast, the storage controller may generate altered shard A1′2011 based on knowledge that original shard A1 2010 contained bit fliperrors, compare the stored CRC value in shard A1 2010 to a CRC valuegenerated from altered shard A1′ 2011 and, based on a match in valuesduring the comparison, make an initial determination that altered shardA1′ 2011 is likely to be free of errors.

As stated above, this may be an initial determination with respect toaltered shard A1′ 2011, not a final conclusion that this data portion isfree of bit flip errors. This is because there may be a small butnon-zero probability that two different inputs result in the same CRCvalue (i.e., a corrupted data portion and its altered version might bothreturn the same CRC value in rare cases, even though the altered versionstill contains a bit flip error). For a more conclusive determinationthat altered shard A1′ 2011 is free of bit flip errors, the examplemethod of FIG. 22 also includes reconstructing 2204 a third data portionwith parity calculations that are performed using the third data portionand a first subset of the plurality of data portions that includes thefirst data portion.

In the example of FIG. 21 , where shard A2 2012 was unavailable, CRCcomparison was used to determine whether the altering corrects the errorin the first data portion. By contrast, here in FIG. 22 data may beavailable for shard A2 2012 and for all other shards, or at least asufficient number of shards that are needed to perform a RAID rebuild ofa shard. More specifically, the first subset of the plurality of dataportions may refer to altered shard A1′ 2011 and any additional shardsthat are required for parity calculations depending on the RAIDconfiguration that is in effect. The third data portion may refer to,for example, shard A_(Q) 2018. In other words, altered shard A1′ 2011and additional shards may be used in a RAID parity calculation thatresults in a reconstructed shard A_(Q), referred to in FIG. 22 as shardA_(Q)′ 2019.

The example method of FIG. 22 also includes performing 2206 a memorycomparison between the third data portion and the reconstructed thirddata portion. Continuing with the example above, the method can includea comparison of the contents of shard A_(Q) 2018 and shard A_(Q)′ 2019.If the memory comparison of shard A_(Q) 2018 and shard A_(Q)′ 2019indicates that these two shards include the same data, then it may bedetermined that the altering of shard A1 2010 to generate altered shardA1′ 2011 resolved the bit flip error that resulted in a corrected shard(i.e., altered shard A1′ 2011).

One or more embodiments may be described herein with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

While particular combinations of various functions and features of theone or more embodiments are expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method of recovering corrupted data throughspeculative bitflip and cross-validation, the method comprising:altering a first data portion stored in a storage device, wherein thefirst data portion contains an error; calculating a hash value using thealtered first data portion; comparing the hash value calculated usingthe altered first data portion to a stored hash value for the first dataportion; and based on the comparing, determining whether the alteringcorrects the error in the first data portion.
 2. The method of claim 1,wherein altering the first data portion further comprises flipping atleast one bit in the first data portion.
 3. The method of claim 2,wherein flipping the at least one bit further comprises flipping the atleast one bit within a header portion of the first data portion.
 4. Themethod of claim 1, wherein calculating the hash value using the alteredfirst data portion further comprises calculating a cyclic redundancycheck (CRC) value using the altered first data portion.
 5. The method ofclaim 1, wherein the first data portion is included within a pluralityof data portions of a RAID stripe, and wherein determining whether thealtering corrects the error in the first data portion further comprises:reconstructing a second data portion of the RAID stripe with paritycalculations that use the altered first data portion; calculating asecond hash value using the reconstructed second data portion;determining that the calculated second hash value matches a stored hashvalue within the second data portion; and based on the determination,determining that the altering corrects the error in the first dataportion.
 6. The method of claim 5, wherein determining whether thealtering corrects the error in the first data portion further comprises:determining that the second data portion is unavailable; and based onthe determination, obtaining the stored hash value for the second dataportion from the storage device, wherein the storage device stores thestored hash value in a primary header associated with the second dataportion.
 7. The method of claim 5, further comprising: determining thata first number of the plurality of data portions of the RAID stripe doesnot contain an error; based on the determination, reconstructing a thirddata portion with parity calculations that are performed using the thirddata portion and a first subset of the plurality of data portions thatincludes the altered first data portion; and performing a memorycomparison between the third data portion and the reconstructed thirddata portion.
 8. An apparatus for recovering corrupted data throughspeculative bitflip and cross-validation, the apparatus comprising acomputer processor, a computer memory operatively coupled to thecomputer processor, the computer memory having disposed within itcomputer program instructions that, when executed by the computerprocessor, cause the apparatus to carry out the steps of: altering afirst data portion stored in a storage device, wherein the first dataportion contains an error; calculating a hash value using the alteredfirst data portion; comparing the hash value calculated using thealtered first data portion to a stored hash value for the first dataportion; and based on the comparing, determining whether the alteringcorrects the error in the first data portion.
 9. The apparatus of claim8, wherein altering the first data portion further comprises flipping atleast one bit in the first data portion.
 10. The apparatus of claim 9further comprising computer program instructions that, when executed bythe computer processor, cause the apparatus to carry out the steps offlipping the at least one bit within a header portion of the first dataportion.
 11. The apparatus of claim 8 further comprising computerprogram instructions that, when executed by the computer processor,cause the apparatus to carry out the steps of calculating a cyclicredundancy check (CRC) value using the altered first data portion. 12.The apparatus of claim 8, wherein the first data portion is includedwithin a plurality of data portions of a RAID stripe, further comprisingcomputer program instructions that, when executed by the computerprocessor, cause the apparatus to carry out the steps of: reconstructinga second data portion of the RAID stripe with parity calculations thatuse the altered first data portion; calculating a second hash valueusing the reconstructed second data portion; determining that thecalculated second hash value matches a stored hash value within thesecond data portion; and based on the determination, determining thatthe altering corrects the error in the first data portion.
 13. Theapparatus of claim 12 further comprising computer program instructionsthat, when executed by the computer processor, cause the apparatus tocarry out the steps of: determining that the second data portion isunavailable; and based on the determination, obtaining the stored hashvalue for the second data portion from the storage device, wherein thestorage device stores the stored hash value in a primary headerassociated with the second data portion.
 14. The apparatus of claim 12further comprising computer program instructions that, when executed bythe computer processor, cause the apparatus to carry out the steps of:determining that a first number of the plurality of data portions of theRAID stripe does not contain an error; based on the determination,reconstructing a third data portion with parity calculations that areperformed using the third data portion and a first subset of theplurality of data portions that includes the altered first data portion;and performing a memory comparison between the third data portion andthe reconstructed third data portion.
 15. A computer program productdisposed upon a non-transitory computer readable medium, the computerprogram product comprising computer program instructions that, whenexecuted, cause a computer to carry out the steps of: altering a firstdata portion stored in a storage device, wherein the first data portioncontains an error; calculating a hash value using the altered first dataportion; comparing the hash value calculated using the altered firstdata portion to a stored hash value for the first data portion; andbased on the comparing, determining whether the altering corrects theerror in the first data portion.
 16. The computer program product ofclaim 15, wherein altering the first data portion further comprisesflipping at least one bit in the first data portion.
 17. The computerprogram product of claim 15 further comprising computer programinstructions that, when executed, cause the computer to carry out thestep of calculating a cyclic redundancy check (CRC) value using thealtered first data portion.
 18. The computer program product of claim15, wherein the first data portion is included within a plurality ofdata portions of a RAID stripe, further comprising computer programinstructions that, when executed, cause the computer to carry out thesteps of: reconstructing a second data portion of the RAID stripe withparity calculations that use the altered first data portion; calculatinga second hash value using the reconstructed second data portion;determining that the calculated second hash value matches a stored hashvalue within the second data portion; and based on the determination,determining that the altering corrects the error in the first dataportion.
 19. The computer program product of claim 18 further comprisingcomputer program instructions that, when executed, cause the computer tocarry out the steps of: determining that the second data portion isunavailable; and based on the determination, obtaining the stored hashvalue for the second data portion from the storage device, wherein thestorage device stores the stored hash value in a primary headerassociated with the second data portion.
 20. The computer programproduct of claim 18 further comprising computer program instructionsthat, when executed, cause the computer to carry out the steps of:determining that a first number of the plurality of data portions of theRAID stripe does not contain an error; based on the determination,reconstructing a third data portion with parity calculations that areperformed using the third data portion and a first subset of theplurality of data portions that includes the altered first data portion;and performing a memory comparison between the third data portion andthe reconstructed third data portion.